{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-03T07:08:23.927587Z",
     "start_time": "2025-04-03T07:08:23.914796Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import struct\n",
    "import os\n",
    "\n",
    "\n",
    "def read_idx(filename):\n",
    "    \"\"\"\n",
    "    读取 IDX 文件 (MNIST 数据格式).\n",
    "    \"\"\"\n",
    "    with open(filename, 'rb') as f:\n",
    "        # 读取 Magic Number 和维度信息\n",
    "        # >ii 表示两个大端序(big-endian)的 4 字节整数\n",
    "        zero, data_type_code, num_dimensions = struct.unpack('>HBB', f.read(4))\n",
    "        # 根据数据类型代码确定 numpy 的 dtype\n",
    "        # 0x08: unsigned byte\n",
    "        if data_type_code == 0x08:\n",
    "            dtype = np.uint8\n",
    "        else:\n",
    "            # 可以根据需要添加对其他类型的支持\n",
    "            raise ValueError(f\"Unsupported data type code: {data_type_code}\")\n",
    "\n",
    "        # 读取每个维度的大小\n",
    "        # '>' + 'I' * num_dimensions 表示读取 num_dimensions 个大端序的 4 字节无符号整数\n",
    "        dimension_sizes = struct.unpack(\n",
    "            '>' + 'I' * num_dimensions, f.read(4 * num_dimensions))\n",
    "\n",
    "        # 计算数据总大小\n",
    "        total_size = np.prod(dimension_sizes)\n",
    "\n",
    "        # 读取数据部分\n",
    "        data = np.frombuffer(f.read(total_size), dtype=dtype)\n",
    "\n",
    "        # 将数据重塑为正确的维度\n",
    "        data = data.reshape(dimension_sizes)\n",
    "\n",
    "        return data\n",
    "\n",
    "\n",
    "data_dir = r'./data/'  # 使用原始字符串避免转义问题\n",
    "\n",
    "# 检查路径是否存在\n",
    "if not os.path.isdir(data_dir):\n",
    "    print(f\"错误：目录不存在 - {data_dir}\")\n",
    "else:\n",
    "    try:\n",
    "        # --- 加载训练数据 ---\n",
    "        train_images_path = os.path.join(data_dir, 'train-images.idx3-ubyte')\n",
    "        train_labels_path = os.path.join(data_dir, 'train-labels.idx1-ubyte')\n",
    "\n",
    "        if os.path.exists(train_images_path) and os.path.exists(train_labels_path):\n",
    "            print(\"正在加载训练数据...\")\n",
    "            X_train = read_idx(train_images_path)\n",
    "            y_train = read_idx(train_labels_path)\n",
    "            print(f\"训练图像形状: {X_train.shape}\")  # 应该是 (60000, 28, 28)\n",
    "            print(f\"训练标签形状: {y_train.shape}\")  # 应该是 (60000,)\n",
    "        else:\n",
    "            print(\"警告：训练数据文件未找到。\")\n",
    "\n",
    "        # --- 加载测试数据 ---\n",
    "        test_images_path = os.path.join(data_dir, 't10k-images.idx3-ubyte')\n",
    "        test_labels_path = os.path.join(data_dir, 't10k-labels.idx1-ubyte')\n",
    "\n",
    "        if os.path.exists(test_images_path) and os.path.exists(test_labels_path):\n",
    "            print(\"\\n正在加载测试数据...\")\n",
    "            X_test = read_idx(test_images_path)\n",
    "            y_test = read_idx(test_labels_path)\n",
    "            print(f\"测试图像形状: {X_test.shape}\")   # 应该是 (10000, 28, 28)\n",
    "            print(f\"测试标签形状: {y_test.shape}\")   # 应该是 (10000,)\n",
    "        else:\n",
    "            print(\"警告：测试数据文件未找到。\")\n",
    "\n",
    "        # 使用 X_train, y_train, X_test, y_test 进行后续处理 ---\n",
    "        # 将图像数据展平\n",
    "        if 'X_train' in locals():\n",
    "            X_train_flat = X_train.reshape(X_train.shape[0], -1)\n",
    "            print(f\"\\n展平后的训练图像形状: {X_train_flat.shape}\")  # (60000, 784)\n",
    "        if 'X_test' in locals():\n",
    "            X_test_flat = X_test.reshape(X_test.shape[0], -1)\n",
    "            print(f\"展平后的测试图像形状: {X_test_flat.shape}\")   # (10000, 784)\n",
    "\n",
    "        # 将它们包装成 scikit-learn 的 Bunch 对象\n",
    "        from sklearn.utils import Bunch\n",
    "        mnist_data = Bunch(\n",
    "            data=X_train_flat if 'X_train_flat' in locals() else None,\n",
    "            target=y_train if 'y_train' in locals() else None,\n",
    "            images=X_train if 'X_train' in locals() else None,\n",
    "            DESCR=\"手动加载的 MNIST 训练数据\",\n",
    "            data_test=X_test_flat if 'X_test_flat' in locals() else None,\n",
    "            target_test=y_test if 'y_test' in locals() else None,\n",
    "            images_test=X_test if 'X_test' in locals() else None\n",
    "        )\n",
    "        print(mnist_data.keys())\n",
    "\n",
    "    except FileNotFoundError as e:\n",
    "        print(f\"错误：文件未找到 - {e}\")\n",
    "    except Exception as e:\n",
    "        print(f\"发生错误: {e}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "错误：目录不存在 - ./data/\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-30T09:04:53.445610Z",
     "start_time": "2025-03-30T09:04:53.394271Z"
    }
   },
   "source": [
    "X_train = None\n",
    "y_train = None\n",
    "X_test = None\n",
    "y_test = None\n",
    "\n",
    "try:\n",
    "    print(\"正在加载数据...\")\n",
    "    train_images_path = os.path.join(data_dir, 'train-images.idx3-ubyte')\n",
    "    train_labels_path = os.path.join(data_dir, 'train-labels.idx1-ubyte')\n",
    "    test_images_path = os.path.join(data_dir, 't10k-images.idx3-ubyte')\n",
    "    test_labels_path = os.path.join(data_dir, 't10k-labels.idx1-ubyte')\n",
    "\n",
    "    required_files = [train_images_path, train_labels_path,\n",
    "                      test_images_path, test_labels_path]\n",
    "    all_files_exist = True\n",
    "    for f_path in required_files:\n",
    "        if not os.path.exists(f_path):\n",
    "            print(f\"错误：文件未找到 - {f_path}\")\n",
    "            all_files_exist = False\n",
    "\n",
    "    if all_files_exist:\n",
    "        X_train = read_idx(train_images_path)\n",
    "        y_train = read_idx(train_labels_path)\n",
    "        X_test = read_idx(test_images_path)\n",
    "        y_test = read_idx(test_labels_path)\n",
    "        print(\"数据加载完成。\")\n",
    "        print(f\"训练图像形状: {X_train.shape}\")\n",
    "        print(f\"训练标签形状: {y_train.shape}\")\n",
    "        print(f\"测试图像形状: {X_test.shape}\")\n",
    "        print(f\"测试标签形状: {y_test.shape}\")\n",
    "    else:\n",
    "        print(\"数据加载失败，缺少文件。\")\n",
    "        exit()  # Exit if data loading failed\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"加载数据时发生错误: {e}\")\n",
    "    exit()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在加载数据...\n",
      "数据加载完成。\n",
      "训练图像形状: (60000, 28, 28)\n",
      "训练标签形状: (60000,)\n",
      "测试图像形状: (10000, 28, 28)\n",
      "测试标签形状: (10000,)\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-30T09:04:58.530426Z",
     "start_time": "2025-03-30T09:04:57.067331Z"
    }
   },
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 将数据展平\n",
    "X_train_flat = X_train.reshape(X_train.shape[0], -1)\n",
    "X_test_flat = X_test.reshape(X_test.shape[0], -1)\n",
    "# 标准化数据\n",
    "scaler = StandardScaler()\n",
    "X_train_flat = scaler.fit_transform(X_train_flat)\n",
    "X_test_flat = scaler.transform(X_test_flat)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-30T09:05:16.381446Z",
     "start_time": "2025-03-30T09:05:04.530703Z"
    }
   },
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# 训练模型\n",
    "model_logistic = LogisticRegression(max_iter=1000)\n",
    "model_logistic.fit(X_train_flat, y_train)\n",
    "# 评估模型\n",
    "accuracy = model_logistic.score(X_test_flat, y_test)\n",
    "print(f\"Logistic 模型准确率: {accuracy:.4f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic 模型准确率: 0.9217\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA 模型准确率: 0.8730\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "model_lda = LinearDiscriminantAnalysis()\n",
    "model_lda.fit(X_train_flat, y_train)\n",
    "accuracy_lda = model_lda.score(X_test_flat, y_test)\n",
    "print(f\"LDA 模型准确率: {accuracy_lda:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP 模型准确率: 0.9763\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "model_mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000)\n",
    "model_mlp.fit(X_train_flat, y_train)\n",
    "accuracy_mlp = model_mlp.score(X_test_flat, y_test)\n",
    "print(f\"MLP 模型准确率: {accuracy_mlp:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Logistic 回归 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       980\n",
      "           1       0.96      0.98      0.97      1135\n",
      "           2       0.92      0.89      0.90      1032\n",
      "           3       0.90      0.91      0.91      1010\n",
      "           4       0.94      0.93      0.93       982\n",
      "           5       0.89      0.87      0.88       892\n",
      "           6       0.94      0.95      0.95       958\n",
      "           7       0.93      0.92      0.92      1028\n",
      "           8       0.87      0.88      0.88       974\n",
      "           9       0.91      0.92      0.91      1009\n",
      "\n",
      "    accuracy                           0.92     10000\n",
      "   macro avg       0.92      0.92      0.92     10000\n",
      "weighted avg       0.92      0.92      0.92     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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KZ1n301pdunhJ7T/qrNRp0qhFq7aaM2um3WU5Xo9efdXk1dftLsMjDR0ySGXLl1euXHnsLsUjlCpdRg0aNo6YP3TooFKnecHWmpyoe88+evmV1yItS5YsuapWr2H9PTg4WBPGj1XZchVsqtDZzIVosuTJI+Z9vG9ervj4+NpYlWfgfPBo2NYe37OyrdkWhsaPH68SJUooceLEypQpkwoVKiR/f3/FiRNHtWrV0s6dO+V0iePGtEJQuJDQm0HuRkio9Wf/1wpowea/tHnv6Yjv5HwhoX7Zc1pXrodY89v+ClCWVAmeeu1OtWf3LuXKnVuxYsWy5jNlzszzCA8hVerUdpfgkTb/vEmbN21U67Yd7C7FIwVfv64J341Vw0Yv212KR+2Tu3fvUvnSxbVpw3p16NjlqdblKTJnzqI1q1ZG3CCdN3eOcuTMpbhx49pdmuNxPng0bGuP71nZ1mwJQx07dtTMmTP16aefWq1Bp06d0uHDh60+1tu3b1fy5MlVqlQp67N7uXbtmgIDAyNNYSHBT/XfseWvc6qW79aG8EqZjPpl72kFXglWyezJrG503b//X6SfMV3p/jp9MdKy0NAwJYgd/anV7WSXLl1SypSpIua9vLzk4+OtwAt0JcF/yxxD+vbuqa49elk3YfDoxowaIT8/P71Uv4HdpXiUTJky68tvvlMGf3/17E4Yisprb75ltZ6Z50dfb9JI4779Wg0bN7G7LDyD2NZgSxj65ptvNGrUKOXKleuuz9KmTasvvvhC3t7e2rBhwz1/x4ABAxQ/fvxIU/Cupzsqz6j52xTN11vrBtbQ8r5V1a52Tn25dKdiRPPRyHeLqPXXm3TxSuSAdiM0TNeCb7Ychbt6PUSxotMca/j4+Cha9MjBMHqMGLpy9aptNeHZ9NUXnylHjhwqWaq03aV4pI0b1mvG9KkaMHiYokWLZnc5HsXc5MmSNZt69xug1atWcLMnCvHjJ9C4SVM0aNgnypgps9KlS68q1arbXRaeQWxrsCUMZciQQUOHDtXVe1zgTpo0SRcvXlT+/Pceaa1z587Ws0a3T9GyRH5Y9UkLCLquCj2W6LVPVmv74QDtPnpe09cdVKeXcum3/We07Pe7hxkMuHRNSeLFjLQsTqxoCr5xs9uc25lQa0Ynud3loCAutvCfW7xogVatXKnihfNb0+JFC9W/X2993KeX3aU53tEjR9S5Y3urVc20buDhu2V+MmxwpJs/htc/zyjgbs8997xWrvhRLVq3jVhfwJPAtuZetjRHjB07VjVr1tS0adNUuHBhpUuXTjFixNDp06e1ceNGq8vb5MmTre5y92K+b6bbefnYc8F8IuCyahZMoxZfbVRoWJjqF09vBR4z3LbhF8NHdYukVX7/JJq76ZBeL3tzJCEjzXNxFCOat85dum5L7U6TPUfOSAMmmHHrzQhzJiQB/6VxEybrRsiNiPnhQwZbz6vVrF3H1rqcztzEavHh+ypbtrzKlCln3awwYvn5WS0euDczYmHbVs2UJs0LKlaipD4dPUJFihbj2YT7mDp5ktKmS68y5crbXQqecWxr7mVLGDJdU3bt2qXFixdry5YtVvjx9fVV9uzZ9cYbb6hkyZIelcqbVslqjRi38JfD1nzFHkvk63PrTl//V/Nbgyh8v3qfzgddV3y/aGpcMoOmrN1vda0zo9GZEAUpX/4CunjpohbMm6satWpr7DdfqVDhoh61PcAzJE2WLNK8efYlQYKESpgwkW01eQLzPiYzvLaZZs2cHrF88Q8rIj3vh7s9/3xSDR42QkMHD7RaiIoULa6+/W+1FCEy816m8WO/0adffGN3KXjGsa25m1eYJ41l/QBxGox76v/N+H7RtWV0XdXp/6N+2x/1gA9ffFhcP20/qe/X7LPmqxdIo7EtS+rSlWD5eHupcq+l2nn0vOxwZvIbcpqVK5ar80ft5Bc7tkJDQvTt+Eny92f4cQAPxwxKg8dAw94j86Y19LFwA/jRsa09npgP0exDGLJJ8oR+ejFDYv28+2+duXjNtjqcGIYMM8Lgju1blTvPi0qUiDv1AB4eYegxca31yLhAfTyEoUfHtvbkwhBDmNnEPGe06NfLdpfhWEmTJrUmAAAA4ElhCBsAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKXmFhYWF6Rly9YXcFnidhgeZ2l+CRAn4ZY3cJAAB4pBshz8yl51Pj6+NldwkeKabvg79DyxAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAXC18+cD9Mfvvykg4JzdpQCIAvsogCeJMPSE7d27Ry83eEnFixTQ8KGDFBYWJrdKFD+2di7spTTJE0Usy5YhudZN6qDjawarf+vakb7/as3C+nVGF51YO1jjB7yhxAli3/U7+7asqZkj3n8q9XuKD957W/PmzLa7DI+wZPEiVa9SUf379VHl8mWseTzYqpXLVbVSOb2YK5uaNKqvA/v3212S47HOHg/76KPjuuPhrF61QjWrlFfBvNn12ssNdPDAzX1y8IC+ypcrS8RUq1pFu0t1rL3PyLZGGHqCrl+/rpbNmipr9uyaMm2WdfKbN9edF6kmyMwe1VRpUyaJWBY9mq9mjXxfv+08omJNBitL+mRWADLKFMqsYR/V00dDZ6tQwwGKGzumpg17N9LvNEHqvfol1GHorKf+73GqRQvna8P6dXaX4RECAwM1qH9ffTfhe02fNVfdevbWyOFD7S7L8Y4cPqweXbuoVZt2+nHlWqVIkUK9e3S1uyxHY509HvbRR8d1x8M5cuSwenfvouat22nJ8jVKniKF+vbqZn22c8d2jfz0S61et9maJk9n/T3r2xph6Ala99NaXbp4Se0/6qzUadKoRau2mjNrptxowsA3NWPp/yItq1Qsm+LFiaWOw2bp4NEz6jlmgd6oXcT6rEn1gho3Z4NW/rxLh08EqMuIuSr2or/VuhRuTLdGGjN5tfWzkC6cP69hgwcpbbp0dpfiES4HBalDpy7KmDGTNZ85cxYFBl6wuyzHO3Bgv1q0bqNKlasqcZIkqt+wsbZv32Z3WY7GOns87KOPjuuOh2NagZq1bKOKlaooceIkqtegkXZs36YbN25o/769ejFffsWNF8+aYseOY3e5jrTuGdrWfO0u4Fm2Z/cu5cqdW7FixbLmM2XO7NquEc36TtGhY2c19KN6EctyZkqpzVsP6crVYGt+655jVuuQkThBHP2562jEd0NCQq0/b4SEWH++VbeYcmVOpe/mbFSVEjn048YdunHj5nfcauiQQSpbvryuXb1mdykeIVny5KpWvab19+DgYI0fN1blytMd4kFKlS4Taf7QoYNKneYF2+rxBKyzx8M++ui47ng4JUtF3if/OnTI2if37tltdfVqXL+OTv99Si/mK6CuPfsoefIUttXqVHueoW3NY1uGrl27ZjWh3z6ZZU5y6dIlpUyZKmLey8tLPj7eCrzgvjtbJgjdKV6cmHctDwkNVYK4sbRl91FVK5UzYvlrtQrrl62HFHjpqmLHiq6ezapr3+HTSpk0gVq+UkbLv22jGNHdm+03/7xJmzdtVOu2HewuxePs3rVLZUsW08b166270Hh4wdeva8J3Y9Ww0ct2l+IxWGePjn304XHd8eiCg69r4vixVovtwYMHlD5DRvUfNEwz5iySr6+v+vfpaXeJjnTpGdrWPDYMDRgwQPHjx480DRk0QE7i4+OjaNGjR1oWPUYMXbl61baanMS05FwPvhFp2bVrN+QXM7pGTFih6NF8tGFyR60a11bt36yoz6etsb5Tq1weKxBVeW+UBn69VNU//FTx48RSk+qF5EbmJkDf3j3VtUcvxYlDc/6jMnezvho7Thn8/dWjW2e7y/EoY0aNkJ+fn16q38DuUjwG6+zRsY8+PK47Ht1no0da+2SdlxqoarUaGv/9NGXPkVMpU6VSxy7dtWnjeuvCH8/utmbLrfTDhw8/1PfSpElzz886d+6stm3bRloW5hNDTmIC2r59e+/qAx0tWjTbanKSgMAgZcsQuek5TuwYun4jRAGBl1X2zU+UPnUStX6tvBLE9dO0Jb9a30n5fAKrlch8J7wL3ba9x5Q2ZWK50VdffKYcOXKoZKnSdpfikczdrKxZs6nvxwNVuUIZ665WvPjx7S7L8TZuWK8Z06dq4uTpHNMeEuvs8bCPPjyuOx6NCTqzZkzVuEnTolxHceLGU2hoqM6c/pubjc/wtmZLGCpdurT++usv6+/3GobPHPxC/nk+JCoxYsSwpttdjdzIYDtzZ+H2h8mOHTtqjb5hNiBIv24/rDfqFI2YN0Nux4jmq3MXgiKWnTh9QbXK5lbzvlMUGnpzWzl6KkCxYka+G2F+ds2vkXdKt1i8aIECzgWoeOH81vyVK1f1w7Il2rZ1i9VahKj9vGmj1q9bq7btO1rzPr4+1p9e3h7bYP7UHD1yRJ07tre2L3O3Hg/GOnt07KOPjuuOh3f06BF169RBnbr1UvoMN/fJ4UMGKEeuPNbACsaO7Vvl7e2tpMmS21yt82R/hrY1W44ov/zyiwoVKqQRI0ZYiTuq6X5ByFPky19AFy9d1IJ5c635sd98pUKFi1pNi5DW/bbPGk3u5eoFrfn2b1XUyp93R4Qe48NGpbTn0CktWL0lYtnSn7Yrc7qkeqdecauV6MPGpazBFH5Yv0NuNG7CZM2at0DTZs21ptJlyurD5i31YYuWdpfmaOnSp9fM6dOs6eSJExo5fJiKFC2muHHj2l2ao129elUtPnxfZcuWV5ky5aw7gWby1PdLPA2ss8fDPvrouO54+H2ydfOmKl22nHXOvHw5yJoyZsqiz0aP0G+//mI9izt4QD/VqFUnYpAAPJvbmleYTUfjs2fPqlGjRvr666+VNm3a/+R3Oq1lyFi5Yrk6f9ROfrFjKzQkRN+OnyR//4xyioQFmj/V/96V38coc9UeOnzi5pvEa5TOpXH939DFy1fl4+2tiu+M0M4DJ63PzHNA2xf0Uq1mn+p/OyJ3rSyYM60Gtq2j3JlT69TZQHUcNjtSYHrSAn4ZI6fq3qWT8hcoqFp16tpdiuOtX/eThg4aoFOnTqposeLq0r2XEiW69VJgRH1Ma9Oy2V3LF/+wItLDtLiFdfb42EefvesO40ZImO0vQW7f+u7rnwVLlmvWzGmaNX2atf7KlC2v5i3bKJafn+zm6+Mlp1npAdtaTF8Hh6EnwYlhyDh16pTV1Jo7z4uOO4g/7TAUlRTPxdeL2dNo058HdSbAMx5SdHIYAgC4m5OvO5wQhjyRE8OQJ2xrhCF4RBjyRIQhAAAeD2Ho2QlDTvcwYYinEAEAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCt5hYWFhekZcfn6M/NPeWpCQllnjyNP12V2l+Bx/vi4kt0leCRvby+7S/A43l6ss8cRHBJqdwkex9eHbe1xXL3OtvaoYkX3sbsEjxQr2oO/Q8sQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDAEAAABwJcIQAAAAAFciDMExzp8P0J9//K7zAQF2l4JnHNsa4FzsnwCeJsLQEzqQV6tcTsePHY1YNrB/X+XNmSViqlm1oq01Os2yJYtUp3plDerfV9Uql7XmjdWrVqhW1Qoq9GIOvd6koQ4e2C+3qp0vhX7qVlpb+lfQhPcLKGXCWBGfJfCLptVdSkVadrsO1TLpq7fyPcVqPW9bu12LD97VgnlzbKnPiVavXKEalcurQJ7seu3lBjpw235ojnfV7zje4W7mHJAnR+aIqUaVCnaX5Eg/LFmkutUra3D/vqpeuaw1b/bFArmz3jWxj0Zt3pxZqle7hkoUKaBOHdoqgFAZpWGD+qnIi9kipno1K2nR/DmRloVPZjnubeQnQ9WyWVN5Kl+7C3jWmINOq+ZNdfzYsUjLd+7YrlGffqk8efJa894+5NBwFwMDNWTQx/r6u4nyz5hJixbM0+iRw5Q9R0716dFVnbv11Iv5C2jIwI/Vt3d3jR0/WW6TJrGf2lXJpKbf/aZzQdfVsqK/BjfKqSafb1bC2NGsoJM6sV+UP5spWRw1KfqCag5fL7e717ZWqUq1iO8sWbRAGzesU8XKVW2t1SmOHDmsXt27qEv3XsqXv4AGD+invj276buJU6zjXesojne4247t2zX6s6+U+59zgA/ngHvun1/9s38u/mf/nL1gqUqXKRfxvctXLuuVhnWVN19+W+t1ok0bN2jwgI81bMRopUmbVgP69lG7Vs01dsL3dpfmOLt27tCwUZ8rZ+5/rsu8fRQ9ejSVvG1bu3L5sl5v/JLyvMi2di/79u7R9KmTNXXGXHkqjsb/MXMXpvJtF1bGjRs3tH/fXuXLn19x48Wzptix49hWo9MEXQ5Suw6drZOfkSlzFuukaFqBPmzRWhUqVVHixElUr34j7dy+TW6ULWU8/f7XeW0/FqgT569q5i/HlO652NZnI1/Jo0V/nLjnz/arl0Pj1h7S4bOX5Xb32tbCXbhwXiOGDdYLadPZWKWzmP2weas2qli5ihInSaJ6DRtpxz/7YecObSMFSUTt5jlgj3UOiBcvnjVxDoh6/2x72/6Z8Z/9M1q06BHnTjOZkFSmbAWlSpXa7pIdZ+H8earzUn0VLlpMKVKkVOv2HfT7b/+zWnAReZ88sH+vFXLixo1nTbFjx765rf0zb6bFC+epVNnySsm2FqWwsDD17d1DTV59XanTpJGnIgz9x7r37KOXX3kt0rK9e3ZbG0yjenVUOH9uNWv6jk6cOG5bjU6TLFlyValWw/r7jeBgTRw/VmXKVVCJUmVUr0GjiO8dOnRQqVN77s72b+w7dUlFMia2QlGcmL56tVgard9zxvqs64xtGvfTX1H+XKPCqZU1RVwdOXdZZbI+J19vL7nZvba1cCOGDlbpsuWUM1duG6t0lpJ37Id/HTqk1GlesP7eLYrjHe4Wfg5o+FJtFcqXSx++/zbngIfYPyeNH6vSt+2fxrVr1zT1+4l64+33bKrS2UzoSZY8ecS8j/fNyzwfHzoC3W7/3j3mSl6vNaqrUkXyqnWz93Tyjn3SbGvTp0zS62+xrd3LrJnTtXv3LqVImUprV69ScHCwPJFjw9Dhw4fliVKlvvvuwcEDB5TBP6MGDB6mWXMXydfXV/1697SlPifbs3uXKpYtoZ83brDu3t8uOPi6Jk34TvUaNpZbw9DSP09qQdti+vPjCsqdJoEGLNhtfXbk3JUof8Yvuo/aVs6oQ2cuK3n8mHq7VDpNaVZI0X0du9vbuq39uvlnbd68SS1at7e7PMcy++GEcWNV/5/9MKrjHe5mnrEKPwfMnmfOAdHUr3cPu8ty9P5ZqWwJq8vXneeCZYsXKkeu3EqRMqVt9TlZ5sxZtGbVSit8G/PmzlGOnLkUN25cu0tzlEMH9ytden/1GTBUU2YusK7LBn3cK9J3fliy0Fp3yVOwrUXl8uUgfTp6hNKkeUF/nzqpiRO+09tvvGKFSE9jy1XR7t27VaJECcWKFUs5c+bUF198oZCQkIjPg4KClC7d/bupmJUdGBgYaXLq/4Cq1WtowvfTrGdgUqZKpY5dumvTxvW6dOmS3aU5SsZMmfXZV2OVPoO/evfoEumzz8aMlJ+fn+q+1EBulCdNApXN/rzqjNignJ1/0MLfj+vbd+4/IEKlnEkVK7qPXvl8s8Ys36/Xv/pF8WJFU938HNjv3NbMsaN/357q3LWH4sSh+9K9fDr6n/2wnjv3w8dVrXpNTZw83bqwMt1tOnXtro0bOAc8zP7Z545zwawZ01S3fkPbanO61958y7o7/3KDl/R6k0Ya9+3Xati4id1lOU6lqjX0zYSpypY9p9Wq0a5jV23etEFBt+2Tc2ZNV516bGv3suLHH3XlyhV9PXa83mvaTJ9/Ndbq1rpgvuc9O2RLGHrjjTeUIkUKLVmyRM2aNdOwYcNUoEABKySFC7+rcS8DBgxQ/PjxI01DBw+QJzD9UENDQ3Xm9N92l+IoXl5eypI1m3r27a81q1cqMPCCtdwEx9kzpqnfgCHyjRZNblQtTzIt/P2Ethy5oMvXQzRsyV5rwATTBe5ekiWIqT8PX9CFKzebrUNCw7T7xEWlShT1iHNu3tZGDBukbDlyqnjJ0naX5libNqzXrOlT1X/QUEVz6X74X+Ec8OD9M3MU54Ijh//S0SOHVahQEbtLdKz48RNo3KQpGjTsEytUpkuXXlWqVbe7LM/ZJ8+cvm1b+0sFCrKt3cupUyeVM2cua5szTOua2eaOH/W8kUVtCUO//vqrxowZo9KlS6tp06basWOHypcvr0KFCmnq1KkRB8P76dy5sy5cuBBpav9R5OZ0pzAh7YelSyLmt2/bKm9vbyVNdqtfr5v98vMmjRw+JGLe18fH+tPby1tHjx5R984fqVPXntZdQrfy9fFSkrjRI+bjxPC1usH53OcZIDPQQoxokXfxFAlj6VhA1N3q3Lytrf9prdW1pHTxgta0dPEiDezfRwM/7m1jtc5x9MgRde3cQZ279XL1fvhvzgHLli6OmOcc8OjnAmP5D0tVvGQp194UexTPPfe8Vq74US1at5XPP+sRt4wcNkjLf7h1XbZjxz/7ZNJk1vyKH5eqWInSbGv3kSxZsrt6ZJlnIZOnSCFPY8sTdWnSpNHatWv10ksvWfPmLuPgwYNVoUIFvfLKK1q9evUDf0eMGDGs6XaXr9+/NckumbNk1ZjRI6yRmEx3QDM0bY1adaxugpDSpkuv9m2aWw9lFy1eQp+PGanCRYpZB6E2LT6wHmgvVaas1T/ViBXL74Fh+Vnzv4PnNbBhTm0rGagzF6+pYaHUOnPxunYdv3jPn1m147R61smmxkVSW3833eaypYirVrtuDrzgRvfa1rr16quQG7e66o4YPtgaRKFGzTpyu6tXr1qvCzBDG5cu6+798HFlzpxVY0aNUJIkzykk5Ib1jquanAOi3D873LZ/fjFmpAoVKaY4/zzvsnH9T6peq67dZXqEqZMnWeuzTLnydpfiSGakwi8/HanEiW/uk8MH9VfVGrUV85998ucN61SN4/99lShVWgMH9NOMaVOsgXZWLP9Bu3ftVNEhw+VpbAlDw4cP11tvvaWzZ8/qvfdujdJhwtDGjRtVvfqz1aRbo2Zta3jaNi2bWUM3moNTi5Zt7C7LMZ57/nkNHDJCw4cO1Mjhg1W4aHH1+XiQNm1YZ603M82ZNSPi+/MXL3fdw7ML/zihdM/H1psl0uq5eDG05+RFfTj+N90IvfcNANM97s2vflWXmlnUtWZWnb54Ta0m/eHqlqF7bWsJEyWK9D3zXEyCBAmVIGFCud3G9VHvhwuXmv0wla21eYoatW6eA1q3/FCx/W6eA1q2amt3WY7cPwcMGaFPhg7UqNv2z/BQvm3rFnXp0cfuMh3PPLcxfuw3+vSLb+wuxbGqVq9lDaLQsW1z+cX2U6ky5dW0eeuIbW37ti3q2I2eAfdjusd99sU3GjZkoDWZG/4Dh3yilB54XvAKe9DDOU/IgQMHtHPnTlWrdvc7KsyGuGzZMtWqVeuRfqdTW4aczDxHgkeXp+syu0vwOH98XMnuEjySt8uHQ38c3rRYPZbgkFC7S/DILsx4dFevs609KjMgEh5drIfo6WjbwPPp06e3pqjEjBnzkYMQAAAAADwKXjgCAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJW8wsLCwvSMuBJsdwVwi5DQZ2a3eWrSfzjD7hI80uEvG9hdgse5EcL++Th8fbzsLsHjhHIueCwhz86l51Pj48X++Tj8oj94vdEyBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAEIWEsaOrQIbEShQnut2lAK52/nyA/vj9NwUEnLO7FADPIMLQEzSwf1/lyZE5YqpRpYLdJXkE1tvDWb1qhWpWKa+CebPrtZcb6OCB/dbywQP6Kl+uLBFTrWoV5Wb1i7yg3wZX08FP62hmu1JKndjvvsuN2gVT6+cBVTXwlRf12+Dq1jzu9sF7b2venNl2l+FY7KP/3pLFi1S9SkX179dHlcuXseYRdWCsVrmcjh87etdnIz8ZplbNm9pSl9P3z1pVKqhQ3hx6/eWGEfvn/Lmz1aBODZUuVlBdPmqn8wEBdpfq+G1tYP++ypszS8RUs6pnHdMIQ0/Qju3bNfqzr7R2wy/WNHXmHLtL8gistwc7cuSwenfvouat22nJ8jVKniKF+vbqZn22c8d2jfz0S61et9maJk9378Vq2udiq3OdHHp9zHoV775MR84GadRbBe+53IgXK5r6v5xXNQetVLneP6rDxP+pe71cdv9THGfRwvnasH6d3WU4FvvovxcYGKhB/fvquwnfa/qsuerWs7dGDh9qd1mOExAQoJbNmur4sWN3fbZv7x7NmDZZ7Tt2saU2pzp65LD6dO+q5q3bavHy1f/sn93186YNGjrwY7X9qJOmzJijoKBLat+6hd3lOn5b27lju0Z9+qXWrt9sTVNmeNYxjTD0hNy4cUP79+1Rvvz5FS9ePGuKHTuO3WU5Huvt4Zg7WM1atlHFSlWUOHES1WvQSDu2b/tn/e3Vi/nyK268eNbk5vWXI01C/e/AOW09fF7Hzl3W1HWHlCFZ3HsuN+LE9FX3KX9o17FAa377kfNK4EdXudtdOH9ewwYPUtp06ewuxbHYR/+9y0FB6tCpizJmzGTNZ86cRYGBF+wuy3E6dWirylWq3bU8LCxM/fr01MuvvK7UqdPYUpuT988PW7ZWhdv2z53bt2nR/HmqVbeeChcppuQpUqpV2w764/f/Wa0hUJTbWvgxzVy3eeoxzVFh6MKFC7p48aKeBXv37LYORA1fqq1C+XLpw/ff1okTx+0uy/FYbw+nZKky1sE73F+HDil1mhci1l/j+nVUtEBuNW/6jqvX357jgSqe5XnlSJNAcWNF05tl/bVm+6l7LjeOB1zRrJ8PW3/39fHSh5Uya9Fvd3c9cbOhQwapbPnyypUrj92lOBb76L+XLHlyVate0/p7cHCwxo8bq3LlPav7zdPQvWcfvfzKa3ctnz1zunbv3qWUKVNq7ZpV1jrETSXu2D8PHTqo1GnSWKHHbHfhvL19rD99fXxtqdMTtrW9/xzTGtWro8L5c6uZBx7TbAlD06dPV9q0aZUgQQK9/fbbVlN43bp1lShRIiVMmFDVqlXT2bNn5ckOHNivDP4ZNWDwMM2et0i+vtHUr3cPu8tyPNbbowsOvq6J48eqfsPGOnjwgNJnyKj+g4Zpxhyz/nzVv09PudWeE4Fa+L+jWtmzovaPqaN86ROp1/Q/77n8dtlTxdf2T2qqdPak6jblD9v+DU6z+edN2rxpo1q37WB3KR6DffTf2b1rl8qWLKaN69dbLUWILFXqu59pvHw5SJ+OGak0aV7QqVOnNGnCOL39xiu6du2aLTU6ff+cNP471WvYWJkzZ9Xa1ausi3tjwbzZyp4jl+LEvdlzwO1SRbGtHTxwIOK6bdbcm8e0fr0965jmFRb+f/wpMSHnhRde0BdffKHcuXNr6NCh+vHHH5U9e3Z999138vHxUefOnXX16lVNnTr1nr/H7NB37tSh3jEUI0YMOdHx48dUvXJ56xmYOHE8q/nQTk5dbyGhT3W3ua+Rw4do08b1mjB5hqJFixbpsxPHj6lm1QpatW6z7esv/Ycznvp/04ScsR8W1RufbrBag1pWzaLS2ZOp65Tf9e0HRe5aXqnf8kg/b1qOejXIrUtXgq3v2uHwlw3kFOaYW69OTXXo2FklS5VW9y6dlL9AQdWqU1dOciPEOfunJ+2jpiXUicxlyq5dOzV8yCDFjhNHI0Z9KqcIddC5wDy4vmjpcqVImUoL5s9V/369tfTHVYofP4HVlalB3Zpq8urreql+Q7tLVcjTvfR84P7588YNmjB5uoIuB6lNiw917epV63pyy59/qPfHA1WtRi27y5SPl5cjt7WortvMwFdr1tt/TDP8ons5r2Voz549ypw5s1555RXlzJlT33zzjXWCNaEoVapUSp48ufr166elS5fe9/cMGDBA8ePHjzQNGTRAThU3bjyFhobqzOm/7S7Fo7De7s9cYM2aMVUfDxx610WWEcfl669WgTSau/mIfj94TkHXbmjAnG164bnY6lI3Z5TLc6ROEOnntx0+r5bfblblPCkV3+/u9es2X33xmXLkyGEFITwc9tF/z8vLS1mzZlPfjwdq9coVCrzAc0MP8vepk8qZM5cVhAxztz5jpsw6FsVoc27fP2fPmKZ+A4fIN1o0a32NnTBZA4d+Iv9MmZU2XXpVrlrd7jI9SlwPPKY99TBkgtDBgwe1c+dOa96cHFatWmW1EoUzQcj0cb0f03pknjG6fTJ3K51i6OABWrZ0ccT89m1b5e3traTJbvVFxd1Ybw/v6NEj6tapgzp166X0GfytZcOHDNAPy5ZEfGfHdnevP3On+7l4MSPmzeAIfjF8FXT1RpTLvb29rGeJeta/NXrcjX/u/DroBrBtFi9aoFUrV6p44fzWtHjRQuvu88d9etldmiOxj/47P2/aqOFDB0XM+/jefH7Dy9tRjzs7ktmeTA+b25nnOMygALi1f3bv9JE6desZsX+Ge+6557Vq+Y9q3qqN1WMJ979u+2HpEo++bnvqT4SZ54I+++wzlSpVSiNHjlTjxo2VK9etC4+PPvpIX375pWbOnHnf32OaL+/sEnfFQc8Gmn6nY0aNUJIkzykk5IY1PGjNWnUUK1Ysu0tzNNbbwzEnudbNm6p02XIqXaas1T/cyJgpiz4bPUJJEifRjZAQDR7QTzVcvP427zujEW8U0PsVMul04FU1KZHO+nP+r0c05NV8dy3fcfS8EseNofHNi+nAqUtasfWEOtfJqdXbT+qikw4wNhk3YbJuhNyImB8+ZLBy5c6tmrXr2FqXE7GP/nvp0qdXm5bNlCZNWhUvUdI6NxQpWkxxeX7jgUqULKVBA/ppxvSpKlmytFas+EG7d+3UwMHD7C7NMftnm+YfWPtnqdv2z1ix/KyWyKlTJlmjZZYuW97uUh0vc5asGjN6hBInSaIQDz2m2TI8RqNGjVSlSpUoR44zy9u0aWN1l/NkNWrVtoZubN3yQ8X2i60y5cqrZau2dpfleKy3h7NxwzprPZlpzqxbz+IsWLJchw4dUNtWzeQXO7bKlC2v5i3byK1MVzj/ZHH1XoWMSho/pjVc9pufbtCWvwKsbnF3LjfPmpw6f1XvfL5BfRrlsZ4XWrX9pJp9s9nuf4ojJE2WLNK8n5+fEiRIqIQJE9lWk1Oxj/57zz+fVEOGj9TQQQOsFqKixYrr44FD7C7LI5juXp9+/rW13oYPGWgNHz1w8PAon/Fwo0332D/nL1luhe2J332rUZ9/bWuNnqJGzZvXbebGRWxzTCtXXi087Jj21AdQeJK4cQs3DqDgKewYQOFZ4KQBFDyF0wZQ8BROHUDByZw0gIIncdIACp7CSQMoeBJHDqAAAAAAAE5AGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK7kFRYWFqZnxNUbdlfgeUKfnf/9T5WXvOwuweN4scoeS5r3pttdgsc5/FUDu0vwSJwOHh3HtcfDtcejuxYcancJHimhn88Dv0PLEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAeGwXzp/Xlj9+1/mAAHkawhAc4fz5AFWrVE7Hjx2NWLZq5QpVr1xe+XNn16uNG+jA/v221ugpRn4yVC2bNbW7DHiw+kVe0G9DqungZ3U0s30ppU7sd9/lD/oMkX3w3tuaN2e23WU43ry5s5UnR+a7JrMc97Zq5XJVrVROL+bKpiaN6nPuxH9m6MB+Kpw3W8RUr2Yla/mPSxerXs3KGjqwr2pVLWfNexLC0BO2d+8evdzgJRUvUkDDhw5SWFiY3SU5TkBAgHXxfvzYsYhlRw4fVq9uXdSydTstW7FGyVOkUJ+e3Wyt0xPs27tH06dOVoeOXewuxSOwf94t7XOx1bluDr0+er2Kd1umI2eDNOrtgvdcfr+fwd0WLZyvDevX2V2GR6harbrWbvglYlq2fI0SJkyofPkK2F2aY5lzZ4+uXdSqTTv9uHKtUqRIod49utpdlkfdiDXn0SYN66lk0YL6ZOhgzgu32b1zh4aP+lw/rt1kTeOnzNbFi4EaNuhjfTF2oiZMna1OXXvp05HD5EkIQ0/Q9evXrYv8rNmza8q0WdbdGe5o3a1Th7aqXLlapGUHD+xX81ZtVLFyFSVOkkT1GzbSju3bbKvRE5gDdt/ePdTk1deVOk0au8txPPbPqOVIk1D/O3BOWw+f17FzlzV13SFlSBb3nsvv9zO4uxvJsMGDlDZdOrtL8QjRokVXvHjxIqYF8+eqbPmKSpU6td2lOdaBA/vVonUbVapc9Z9zZ2Nt59z50DdizXmhVfMPlDVbdn0/daZ1XpjPecFy48YN7d+/V3ny5VfcuPGsKXbs2LocFKTWHTopg39G63v+mTIr8GKgPAlh6Ala99NaXbp4Se0/6mxdnLZo1VZzZs20uyzH6d6zj15+9bVIy0qWLmMFoHCHDh5S6jQv2FCd55g1c7p2796lFClTae3qVQoODra7JEdj/4zanhOBKp7leeVIk0BxY0XTm2X9tWb7qXsuv9/PILKhQwapbPnyypUrj92leJxr165p8qQJeuud9+wuxdFKlS6jBg0bR8wfOnSQc+cj3Ihd/895od1HnazzgrkpO3f2LNtqdJJ9e/eYu656rVFdlSqcV62bvaeTJ44rabLkqly1hvWdG8HBmjzhO5UuW0GexFFhqHnz5jp37pyeFXt271Ku3LkVK1Ysaz5T5sz03Y3Cg+7yBQdf18RxY9Wg0a0DPCK7fDlIn44eoTRpXtDfp05q4oTv9PYbr1gXEIga+2fU9hwP1MJfj2plr4ra/2kd5UufSL2m/3nP5ff7Gdyy+edN2rxpo1q37WB3KR5pyaIF1v6aMmUqu0vxGMHXr2vCd2PVsNHLdpfiMTdizXkhJ+eFKB06uF/pMvirz4ChmjJrgXx8fTWwX6+Iz/fu3qWq5Uvo503r1aZDJ3kS36f9H5wwYcI9Pxs3bpzSp0+vJEmS6LXXIm+gdzIXeXde6IX5xFCMGDHkFJcuXYp04Pby8pKPj7cCL1xQvPjxba3Nk3w6aqRi+fmpbr0GdpfiWCt+/FFXrlzR12PHK378BHrrnfdVv04Nq1tJvfoN7S7Pkdg/o2aCTMU8yVWp73Ir5LSslkWTW5dQ18m/R7nczN/rZ8w8bp6v+vbuqa49eilOnDh2l+ORZkyfqg+atbC7DI8yZtQI+fn56aX6nDsf9kbspaC7zwvenBcspvUnvAXIaN+xq16qUUlBly4pdpw4Vve40V+O1ehPhqhfz64aNHy0PMVTbxmaMmWK3njjDQ0cOFCrV6/WqlWrIibTrWfjxo3W8gcZMGCA4sePH2kaMmiAnMTHx0fRokePtCx6jBi6cvWqbTV5mk0b1mvm9KnqP3iookWLZnc5jnXq1EnlzJnLCkKGr6+vMmbKrONHbz0UisjYP6NWq2Aazd18RL8fPKegazc0YPY2vfBcbHWpmzPK5TlSJ7jnz5jPIH31xWfKkSOHSpYqbXcpHunw4b+sgQEKFS5qdykeY+OG9VaAHDB4GOfOR+Dr46to0SOvrxjRY+iqy88LUTHPDIWGhurMmdMRwTFzlmzq3ru/1q5eqcDAC/IUT71laMmSJfr+++/Vvn17qxly0KBBEXfK5s6dq2HDhinNQzz83blzZ7Vt2/auliEnMQFt3769kZaZB804MD2co0eOqEunDurSvZcyZPC3uxxHS5Ys2V0tpSdOHFeBgoVsq8np2D+j5uvtpQSxbx1L48T0lV8MXyvkPBcv5l3Lvb297vkz5jNIixctUMC5ABUvnN+av3Llqn5YtkTbtm6xWotwfz8sXWIFSbfvm49y7uzcsb21bWXw59z5KEzrz/69kc8LQZeD5Mu2pxFDBylHrlwqX7GKNb9j+1Z5e3vr2JHDmj9nplq0udkF2MfnZrTw9nLUkzj3ZUulTZo00Y4dO6xuPeZu2dKlSx/5d5jucLePMmMmJ3WRM7LnyKmtf97qN3/s2FFrpBJzEYb7M3dhzCgvZcqWU+myZa1nYszEEJdRK1GqtDWK0IxpU3Tq5EnrQePdu3aqaPESdpfmWOyfUdu874yqvphS71fIpLqF0mh8i2I6feGq5v9yJMrlO46ev+fPmM8gjZswWbPmLdC0WXOtqXSZsvqweUt92KKl3aV5hA3rf1J+buw89LmzxYfvq2zZ8ipTppx1g8dMnDsf/rywZcut84IZcts8e+X284KRKXMWfTFmpH7/36/6dfMmDRvcX9Vq1FbGzFk1Z9Z0zZ01XadOntBno4arUJFiihPXc0YUfeotQ+HMuwLGjh1rdY9r2rSpChYsqJCQED1L8uUvoIuXLmrBvLmqUau2xn7zldXMb7rn4P42rl9nDa9tptkzZ0QsX7RsuTVaGiIz3eM+++IbDRsy0JrMkKoDh3zCw8b3wf4ZNdPdzT9ZXL1XIaOSJoipXccC9eanG7TlrwCr69udy2+EhN3zZ8xnkJImSxZp3jzHkSBBQiVMmMi2mjzp4n7rlj+th93xYOYdVubGmJnMCKPhFv+wgvPBQ3gxX35dinRe+FqFChdx/XnBqFqjljWIwkdtmssvtp9KlSmvD1q0VqxYfuo/+BONGDpQoz4ZosJFiqln34HyJF5hDrhdYLr39OnTx3qeaN26ddZLwh7H1RtynJUrlqvzR+3kFzu2QkNC9O34SfL/Zyx2Jwi1/3+/R/IS3X8elZcDV5nT908jzXu3LmjwcA5/xQPjj4PTwbNxXPMETrr2yJsjS6QbratWLFeXju3l5xdboaEh+mbcxIh36NjpWnCo3SV4pIR+Pp4Rhv4rTgxDxqlTp6y+lbnzvKhEiZx1J9BJByRPQhh6di4anLx/GoShR0cYejycDp6d45rTOf3a4+9Tp6yX1ebOk9cx5wXC0JMLQ96P8ubZd999N9KyM2fOqEaNW8PshQsM9Kw3zz5pSZMmVZmy5R2zQwG4hf0TAHC7563zQjnOCy7x0GHIDNW7bNmySA/hbdu2zVp+O/Pcj3lPEAAAAAA8M6PJBQQEKEOGDBHvAVq8eLEaNmyoo0eP6uDBg9Yy85BZ9Dve3QEAAAAAHjma3OXLl62R38z7gH744Qf5+/tbyxYtWqSdO3da3eJatmyp2rVr6+2331bMmLfeRQEAAAAAHtsyZFp+Ro4caQWg4OBga9mIESP05ptvWq1E7733nvbv36+iRYtaL1M17+oAAAAAAI8PQ5kyZdKuXbvUu3dvlStXzmoRmj9/vtUaFN4KlDJlSqtV6JdffqFlCAAAAMCz88zQoUOHdPjwYaub3GuvvaaOHTtGPBu0Zs0a66WpJhD9edsb3QEAAADA48OQ6fq2ceNGpU6dWoMGDVLr1q117tw5hYaGKn369FY3OjNfoECBJ1sxAAAAADytARTCR5IzQ2mnSpXKej5oz549atu2rYKCgqyAlCJFCs2ePVtZs2b9L+oCAAAAAPtbhtauXasqVaooXbp0OnbsmJ5//nnr+aEff/xRPXr0sAZVyJYtm6ZOnapr16492YoBAAAA4GmFoWLFilmjx508edL604gVK5beffdd6/1C0aJF07p16zRz5ky9+OKLunHjxn9RGwAAAADYG4bMi1TNMNqbNm3St99+q++//95aXqdOHU2ZMkUXL17Uc889Zy0LCwuz5gEAAADgmXhmyDADJUyfPl0ZMmSw5nPnzq0+ffpYrUThvLy8It5FBAAAAADPRBgyChUqFGneDKIQzrQIxY0b97+pDAAAAACcMLS2GULbDKQQ/nczgly4q1evqkuXLnrhhRd0/PjxJ1MpAAAAANjRMmQCUPny5a33DXl7eyswMNBabp4jatKkiWLGjKkxY8YoWbJk/2V9AAAAAGBvGPL19ZWfn1/EfIwYMaw/EydOrPfee0/t27e3BloAAAAAgGfumaHo0aNH/N28T8h0jQvXvXt3608zmIJpKTKDLQAAAADAMxGGzLDZt48aFzt27Lu+89NPP+nnn3/WwoUL/5sKAQAAAMAJo8nd3krUtWtXa8CEP/74Q1WrVrWWz5s3T4MHD/4vawQAAAAA+0aTM93ibn+Zavi7hP73v//p1VdfVY4cOTR27FhrkIX169f/95UCAAAAgB0tQ9GiRdOiRYusQLRjxw5rGG3Tbc68V2j//v1asWKFevXqpdOnT6tjx47/ZY0AAAAAYF8YMsNplytXTnv37tU333xjvXOobt261tDa06ZN00svvaTatWtb7xwCAAAAgGcmDHXq1MkaKe7ChQvasmWLRowYoW3btun111/X6tWrrckICQmx3kU0YMCAJ1k3AAAAADydZ4ZMNzkzaIKZDh06pOHDh1vd42bPnh3x3iEzme+ZdxIBAAAAgJN5hd0+XvZD2Ldvn4YMGaIvvvhCy5cv16hRo6zR5L7++mtVrlxZdrpyc0wH4Inz8rK7As9zNTjE7hI8UnTfh75nhX+UGbrW7hI80qp2Je0uwfNwLngsN0Ie6dITkny82dgeR+zoD15vj3yWjR8/vkqXLm29Z6hChQpasGCBPv/8c2sKDQ19rEIBAAAAwPEtQ05GyxCeFlqGHh0tQ4+HlqFHR8vQ46Fl6DFwLngstAw9OlqGHNIyNGnSJGskuc2bN0dafuXKFWXPnj1i3nSf+/nnnx/lVwMAAADAU/VIYah79+767LPPrBet3s4MmnDs2DHr76arXMuWLbVq1ar/tlIAAAAA+A898rBvv/32292/xNfXGmXOGDt2rHx8fNShQ4f/pkIAAAAAsDsMmUET7se8cLVnz576/vvvrUAEAAAAAM9My1BgYKAqVaqkdOnSKVWqVEqdOrU1GTt27FDu3Lmt0eYAAAAAwOPD0OnTp63ub3///bcVhqpVq2a1El2+fFl//vmnlixZojNnzmjEiBGaNm3ak68aAAAAAJ5GGOrYsaPWr19vBSDTGtStWzerK5xpHSpatKj1nUSJEil27NiqXr26Vq5cSTc5AAAAAJ4/mtzw4cO1a9cuJUmSxJrfu3evmjdvrpCQkEiDKJgXr8aJE0eDBg16chUDAAAAwNMKQwkSJLBahcIHUHj77bfVpUsXlShRQo0bN9a4ceMivjtw4EArPJl3DwEAAACAUz3Wq81Hjhyptm3basyYMVqzZo3Kly+vsLCbbxPOmTOn0qRJo4ULF/7XtQIAAACAPWHIBB4Tgnbv3m21Ek2ePNkaWME8R2Rethqudu3amj179n9XJQAAAADYObR2o0aNrNHkzPuEvL29tWHDBmv5tWvXrCmcGVShUKFC/3WtAAAAAPCf8QoL79/2L5hfsXPnTqt1KEeOHLLLlWDb/tNwmQe8fxhRuBp8a8AVPLzovo/Vm9nVygxda3cJHmlVu5J2l+B5OBc8lhsh//rS03V8vNnYHkfs6A9ebw99lr1x44bmzp0b5Wemy1zGjBlVrFgxa37jxo1W6xEAAAAAOJX3o7T+mJHi7iVatGjWZDRt2lRTpkz5byoEAAAAADufGQoPO4sXL7bCTsyYMe/6jnmO6KeffrKeK3r11Vf/61oBAAAA4D/zyJ3RTfe3mjVrWs8HmQEVSpUqZbUaTZw40fpz+vTp+uijj6yXsAIAAACAUz1WYkmdOrX8/Pzk7+8vHx8fxYoVK2L0uFy5cql+/fr/dZ0AAAAA8PTDkGnxGTBggC5duqSjR4/e97vvvvvuf1UbAAAAANjbTS44OFh//vmndu3apb59+z65agAAAADASWEoevTomjZtmvLnz68vvvjivt8dOnSoDh069F/VBwAAAADOGEDBvFPo7Nmzun79uv7++2+dO3fO+vuBAweszy9evGgFIgAAcFO8WL7KmTKe4sdicCHAKc6fD9Cff/yu8wEBdpcCGz3Wq81HjhypY8eOqXfv3ho7dqz199y5c1tBqX379po3b56CgoLkdgvnz1Xl8qVVpEBevf/OGzp27P7PW+Em1tu/88F7b2venNl2l+F4rT58TwvnzXno5W5nLhqqVSqn47ftj6tWrlD1yuWVP3d2vdq4gQ7s3y+3qpYzqX7uXOquySyvkPU5zXq/oDpU9Nf8ZoWt+XDtKvhH+v7MpgXldta2Vjnytjawf1/lzZklYqpZtaKtNcLz/bBkkepWr6zB/fuqeuWy1ryxeME8Va9UViUL59OH772p48eO2V2q4/bP6nfsnwsXzFPVCmVUrOCLavqOWWdHn80wZIbSNs8O1a1bV9euXbNagO6czHfixo2revXqWd3q3OzI4cMaM2qEPhn1qWbPX6TkyVOoR9fOdpfleKy3f2fRwvnasH6d3WU43tJFC7Rpw7qHXu52AQEBatmsaaSLArOv9urWRS1bt9OyFWuUPEUK9enZTW61bPvfKjd8XcRUY8xGBVy+rn1/B6ldRX+9P+kPvfbdbxq4dI+alUkf8XNZksVRm+lbI37utbH/k5tFta0ZO3ds16hPv9Ta9ZutacoMbvg86GbFvr171KRhPZUsWlCfDB1sDYaFmy4GBmrIoI/11XcT9f302erSrZdGjxymo0cO67MxIzV0xGhNn7PAugbp3YNrkNv3z1Z3ngvMOhs1QsNGjtHMeQutc0HPbp2fzTB048YNFS1a9J6fm4BkvmO8/fbbKly4sNxs164dypk7t7Jmy27tTLXq1NXhvw7aXZbjsd4e34Xz5zVs8CClTZfO7lIc7cKF8xo5fLBeSJvuoZZD6tShrSpXrhZp2cED+9W8VRtVrFxFiZMkUf2GjbRj+za51Y3QMF26FhIxVcmRTKt3n1HA5WB9sny/Dpy5bH1v76kgxYt5s6ucj5eU4bnY+v3w+Yifu3w9RHL7tlYl8rZmri3279urfPnzK268eNYUO3Yc22r0hABpHl9o1fwD61z6/dSZVqvt/LkEyHBBl4PUtkNn+WfMZM1nzJzFCki7d+1Uzly5lSVrdiVLnkI1aplrEJ6DD9e5Q1tVumP/3L1zh7XOwq/batauq788bJ09dBgygygMHz78np+bl6zOnDnT+nuOHDmULVu2hy7CDNm9d+9e64Wuz4r0Gfz1y8+btGvnDqvVbNqUySpUpJjdZTke6+3xDR0ySGXLl1euXHnsLsXRRg4brNJlyytHztwPtRxS95599PKrr0VaVrJ0GSsAhTt08JBSp3nBhuqcJ7qPlxoVSKnxGw/r74vXrFYjw8fbS00KpdKq3Wesef/nb17QT3w7v9a0L64RDXMqabwYktu3tVcib2t79+y2WjUa1aujwvlzq1nTd3TixHHbavSEmxXrf1qrSxcvqd1HnZQ6TRrrxsXc2bNsq9FpkiVLrirValh/vxEcrEnjx6p0uQpKlz6Dftm8ybrAv3TxomZMm6xChe/dEOA23aLYP63rts23rtumT/1ehYsUffafGYqKeflq+fLlH/i9TJkyRTxPdOTIEVWtWlUJEiRQ5syZrS527733nq5cuSJPlyGDv8pXqKRG9euoRJH82rr1T7Vt39HushyP9fZ4Nv+8SZs3bVTrth3sLsXRfv3lZ/26eZOatWr3UMtxU6rUqe/7eXDwdU0cN1YNGjV+ajU5WaXsSbXtWKBOXLgWsSzj87G1pGURFUqXSJ8s32ctS5vETwfPXFb3eTvV6OtfdSMkTJ0q37xT7VZRbWsHDxxQBv+MGjB4mGbNXWTdfO3Xu6ct9XnKzYo9u3dZvSxixYplzWfKnNnVz/Tdi1lPlcqW0KaNG9SuQ2frwr5c+Up6pdFLKlO8oLZt3aJWbT+yu0xH75/pzTqrUEkvN6irUkULaNuWLWrTrqM7w9DD2rdvn0JCbnYDMMHHHNTMSHTnz5/XkiVLtG7dOnXt2vWBv8d0ywsMDIw0mWVOseXPP7RmzSpNnDJDGzb/pspVqqv5B+/SZ/cBWG+Pzmz3fXv3VNcevRQnDl1H7reeBvbtpY+69Iy0nu61HA/v01EjFcvPT3XrNbC7FEeokze5Zv9+ItKyvX8HqfmULTpwJkjdq2WxlpkWo7cn/K6dJy7qxIWrGvrDXhVKl1Cxo/vYVLkzVa1eQxO+n6bsOXIqZapU6tiluzZtXG/1KkHUF6iXgi4pZcpUEfNmgCtvH28FXrjwlKtztoyZMuuzr8ZaF/R9enTR1i1/6Kc1qzRu0jSt2fir1SWsVfP3uQZ5wHXb2jWrNGHydK37+X+qVLWaWnz4nkets6cehswOaSZjzZo1Gj16tNKkSaN48eJZLUtjxozR7NkP7tc6YMAAxY8fP9I0ZNAAOcWypYutfs85c+aSn19sNW/ZWseOHtXu3bvsLs3RWG+P7qsvPrO6ppYsVdruUhxt7FefK1v2HCpestRDLcfD2bRhvWZOn6r+g4cqWrRocrtUCWMqVcJY2nzo7qF695y6pD4Ld6tkpsSK+89zQ7e7eO2G1ZUucZzoT6lazxQ3bjxrwKYzp292P8TdfH18FS165P0xRvQYz9TjCP8Fcz2aOWs29ezbX2tWr9TsGdNVoXJVZf/nGuSD5q10/OgRqwUJUfth6RJVqlxVOf5ZZ81amOs2z1pnT/2FByYpbtiwQSVKlFDKlCmtdxa98MKtfubmZGr6HD5I586d1bZt20jLQr2d09fatH4FnDsXMW+6Bl65clmh/7SKIWqst0e3eNECBZwLUPHC+a35K1eu6odlS6zmfdNahJuWLVmk8wHnVK54IWv+6tUrWv7jUiVKlDjK5Tu2bdVHXXvYXLWzHT1yRF06dVCX7r2sLq6QymV5Xuv3nVVI6M27ovleSKCi6RNp9Kqb7+ILXx4aFqZW5TJY3elW7DptLcuWPK71+alA5/RycIKhgwdYz0KawTqM7du2ytvbW0mTJbe7NMeKFz++9u/de9egAb7csLCYZ5M3rP9Jrf7pWu7rc6s19tzZs3dcg1yxwjeiFhJyQwEBgXets/BeYJ7gqYehZs2aqU+fPtq2bZsVjJo3b26FI2PGjBlWF7mmTZs+8PfEiBHDmm53JViOkSfPi+rVo4smThinxIkTa86sGUqcOInVJIt7Y709unETJutGyM2RHI3hQwYrV+7cqlm7jq11Oc2X301UyI1bB+dRnwyx7mSZu4BRLa9ek/V3P+YOsxnBqkzZcipdtqwuX775LGisWH4Rrf9uVCR9Qi3ccjJi/q+zlzXkpew6EnBFG/afU9NSafXzwQAFXQuxWoo+KJVWZ4OuWy1C5p1Di7ae1LUbXHjdLnOWrBozeoQ1aqG5wBo8oJ9q1KoT8TwM7ma6FM6ZdXNQK8MMuR18/brViwZS2nTp1aFNc2vQl6LFS+iLMSOtwZqKFC2mPr26afLEcUqUOInmzZ6pRIkTK+M/o87hbnnyvqjePbpqUvh1m7XOPOu67amHIdMtzjAHtC1btmjnzp0Rn23atEndu3fXq6++Kk9XqUpVHTp0UJMnjtfp06flnzGjho0YTTeSB2C9PbqkyZJFmvfz81OCBAmVMGEi22pyoqRJ71hPsW6up3stT5Aw4VOu0LNsXL/OGl7bTLNnzohYvmjZcqW47VkFN4nh663sKeJpwJI9EcvOXLquznN2qE35DGpZNr02HQhQr/k3u48s2XZK6RL7WWHJDKm9Zs8ZfbaGVwncqUbN2tZ21qZlM8WOHVtlypVXi5Zt7C7L0V7Ml1+XLl3UgnlzVaNWbY395msVKlzEGuwK0nPPP68BQ0bok6EDNWr4YBUuWlx9Ph5kHffNsNBTvp+gM6fPKIO/vwYPH02L2n1UrPzPddsks85uXrcN/WSUR123eYV50hNOD+CkliE821x84/uxXQ32nCZzJ4nu+9Qf7fR4ZYautbsEj7SqXUm7S/A8DjoX5M2RJdLNiFUrlqtLx/bWcxyhoSH6ZtxEa1Q+JzAjJ+LRmNZjPLrY0b2c1zIEAACA/9bv2yI/sG5a0OYtWqbt27cpd568SpSI3gJAVAhDAAAAz6Dnkya1JgD3Rv8LAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSl5hYWFhekZcDn5m/ilPjZe87C7BI4WJbe1ReXuxrT2OGyFsa4+K/fPxZGk9z+4SPM7+0XXsLsEjhYayjz4qb2/OoY8jpu+Dv0PLEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXCEAAAAABXIgwBAAAAcCXC0BNw/nyAqlUqp+PHjkYsW7VyhapXLq/8ubPr1cYNdGD/fltrdLKF8+eqcvnSKlIgr95/5w0du2094sHb2v2WI7K9e/fo5QYvqXiRAho+dJDCwsLsLsmRVq9aoZpVyqtg3ux67eUGOnjg5vFr8IC+ypcrS8RUq1pFu0t13HqrVaWCCuXNoddfbhix3ubPna0GdWqodLGC6vJRO50PCJBbvVQwtTZ/XEl7Pqmhqa2KKVUiP2t53wa5dOzzOhHTut4VrOUNCqeJtDx8MstxE8e1h2edKytHfa4c+ckwtWre1Ja6PMXeZ2RbIwz9xwICAtSyWVMdP3YsYtmRw4fVq1sXtWzdTstWrFHyFCnUp2c3W+t0KrOuxowaoU9GfarZ8xcpefIU6tG1s91lecy2dr/liOz69evWesqaPbumTJtl3aCYN3e23WU5zpEjh9W7exc1b91OS5bfPH717XXz+LVzx3aN/PRLrV632ZomT2f9hTt65LD6dO+q5q3bavHy1f+st+76edMGDR34sdp+1ElTZsxRUNAltW/dQm70QpLY6lgrm976YpNK91muo2cv65PXX7Q+y5UmgV4ds0FZ2y60pkr9V1nL5/xyJGKZmfJ3XqKzF69p074zNv9rnIHj2sO737ly3949mjFtstp37GJLbZ7g+jO0rRGG/mOdOrRV5crVIi0zdwObt2qjipWrKHGSJKrfsJF2bN9mW41OtmvXDuXMnVtZs2W3glCtOnV1+K+DdpflMdva/ZYjsnU/rdWli5fU/qPOSp0mjVq0aqs5s2baXZbjmONXs5ZtVLFSFSVOnET1Gtw8ft24cUP79+3Vi/nyK268eNYUO3Ycu8t11Hr7sGVrVbhtve3cvk2L5s9Trbr1VLhIMSVPkVKt2nbQH7//z7pD7TY5UsfXbwfPaduRCzoecEXTNh5W+ufjyMfbS5lTxLMCTuCVYGsKunbD+pngkLCIZWaqVziNlvxxXIfPXLb7n+MIHNcennWurHL3udK0bvTr01Mvv/K6UqemxdEN25q3U9LlN998o65du+qrr77S4cOH5am69+yjl199LdKykqXLWAEo3KGDh5Q6zQs2VOd86TP465efN2nXzh26ePGipk2ZrEJFitldlsdsa/dbjsj27N6lXLlzK1asWNZ8psyZ6b4ahZKlylgX8uH+OnTz+LV3z27roqFx/ToqWiC3mjd9RydOHLe1Vicpccd6O3TooHXBYEJPsuTJI5Z7e/tYf/r6+Mpt9py4qGKZn1P2VPEVN6av3iiVTj/tOq2sKePJy0v6oUtZ7RtZU5OaF1WKhDf309vF8PXW22UyaPTSPbbU70Qc1x6eda585e5z5eyZ07V79y6lTJlSa9esUnBwsC31Od2eZ2hbsz0MmbuLpUuX1vDhw7V//3599913ypIlixYsWHDfn7t27ZoCAwMjTWaZ3VKlTn3fz4ODr2viuLFq0KjxU6vJk2TI4K/yFSqpUf06KlEkv7Zu/VNt23e0uyxHute29qBtEDddunRJKVOmipj38vKSj4+3Ai9csLUuJ7OOX+PHqn7Dxjp48IDSZ8io/oOGacacRfL19VX/Pj3tLtGx623S+O9Ur2FjZc6cVWtXr4roW79g3mxlz5FLceLGldvsPXlRi347rh+6ltWuT2oob7pE6jNrqzImi2sFpWZjf1GZPst1IyRUg5rkuevnaxdIrd8OBujoOVqFwnFce3hRnSsvXw7Sp2NGKk2aF3Tq1ClNmjBOb7/xiiOuL53m0jO0rdkShmrVqqVDhw5Zf1+2bJmVKrdu3aqpU6dq48aNGjhwoNq0aXPf3zFgwADFjx8/0jR00AA53aejRiqWn5/q1mtgdymOtOXPP7RmzSpNnDJDGzb/pspVqqv5B+967EN5cC4fHx9Fix490rLoMWLoytWrttXkdJ+NHik/Pz/VeamBqlarofHfT1P2HDmVMlUqdezSXZs2rrdOkIh6vdV9qYFeeeNN607zKw1f0luvNtb4sd+oQeOX5UYvpkuo8rmSqdrA1crYer7m/XJUE5sV1ZxfjqrG4DX686/zOnL2srpO+1OlsiZVnJiRW89eLZlWE3+iG/XtOK79OyuW/6grV67oq2/H6b2mH+qzL7/VpYsXrYGd8Oxua7aEoaxZsypPnjzq3bu3zpw5o7Jly1orNdxLL72kkydP3vd3dO7cWRcuXIg0te/o7AftN21Yr5nTp6r/4KGKFi2a3eU40rKli60+vDlz5pKfX2w1b9lax44etZqsgf+SuYESEHAu0rLLQUHsm/dggs6sGVP18cCoj19x4sZTaGiozpz+25b6nLzeZs+Ypn4Dh8g3WjTFj59AYydM1sChn8g/U2alTZdelatWlxvVzJdK8389qj/+CtDlayEaNH+HNaiC6TZ3u8DLwdZzREnjx4xYlva52Er7XBz9tJPt7XYc1/6dv0+dtK4/zH5qmBbvjJkyM6rtM76t2RKGTMvPunXr9OOPP6p58+aaNWuWFWbCLVq0yApL9xMjRgzFixcv0mSWOdXRI0fUpVMHdeney+oKhqiFhITo7JlbowIFBQXpypXLCg0JsbUuPHtMi8bWP/+MmDcnO/P8ojnAI7KjR4+oW6cO6tStl/VcnzF8yAD9sGxJxHd2bN8qb29vJU1263kYtzPrrXunj9SpW8+I9Rbuueee16rlP1qD69x+M9BNTMBJEvfWedu0/MSK4aNe9XKqxospI5bnSZtQIaFhOnbuSsSyGvlSasXWk7oRSq+B23Fc+3fM8evqHS0b5llIM9gJnt1tzbZnhnLkyGEFomHDhunYsWNKlSqVypcvr2LFiqlTp07WM0TPCrNjmeEHy5Qtp9Jly1p9Us1E16+75cnzolau+FETJ4zT4kUL1Kblh9ZITObODPBfype/gC5euqgF8252fxj7zVcqVLioay9M73f8at28qUqb41eZW8evjJmy6LPRI/Tbr79o88+bNHhAP9WoVSfiYVq3M+utTfMPrPVWqszdx/2pUyYpbbp0Kl22vNzqlwNnVTVvCr1bNoNqF0ilsU0L60zgNU3fdNgacruQf2IVy5xEfRvk1vSNf+lq8K2bYqWzJdX6Padtrd+JOK79OyVKlrKeh5wxfapOnTypyd9P0O5dO1WsWHG7S3OcfM/Qtmb78DXvvfeemjRpYrUOmVCUOnVqVatWTQkTJtSzYuP6ddYwq2aaPXNGxPJFy5YrxW0Pn0GqVKWqNerS5Injdfr0aflnzKhhI0Z7ZLMrnM10f+jRq686f9ROw4cNtlofvx0/ye6yHGfjhlvHrzmzbh2/FixZrkOHDqhtq2byix1bZcqWV/OW93/W00023WO9zV+yXHHjxtXE777VqM+/lpvN//WYMiSNq3fK+uv5+DG1+3ig3vnqZ209fF4ZksbR2A8KK+jqDWvo7IHzdkT8XMxo3sqbNqE++v53W+t3Io5r/47pHvfp519bLxAdPmSgdTN24ODhXKs949uaV9gz1DxxOfiZ+ac8NV7ysrsEjxQmtrVH5W3GynUgM2KQ6eKVO8+LSpQokZzmRgjb2qNi/3w8WVrPs7sEj7N/dB05kdOPa6F0b3xk3t6cQx/HHeOuOLNlCADslDRpUmsCgGcFxzU8LUmfgW3N9vcMAQAAAIAdCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXIkwBAAAAMCVCEMAAAAAXMkrLCwsTM+IqzfsrsDzBN8ItbsEj+Tj7WV3CZ6HVfZYQtlFH5mvDxsbno70zWfbXYJHOjCmrt0leJzAK8F2l+CRno8b7YHfoWUIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmEIAAAAgCsRhgAAAAC4EmHoCVu1crmqViqnF3NlU5NG9XVg/367S3Ks8+cD9Ocfv+t8QIDdpXjUOqtWuZyOHzsasWxg/77KmzNLxFSzakVba3SyeXNmqV7tGipRpIA6dWirALa9KK1etUI1q5RXwbzZ9drLDXTwwP77LsfdOBfgv/RSodT6pX9l7R1RU9NaFVeqxH6RPu9SO7vGf1jknj//fYtialAkzVOo1PN88N7bmjdntt1l4CkiDD1BRw4fVo+uXdSqTTv9uHKtUqRIod49utpdliMtW7JIdapX1qD+fVWtcllr3ti3d49ee7m+yhQvpJHDhygsLMzuUh3DXLi3bNZUx48di7R8547tGvXpl1q7frM1TZnBQT0qmzZu0OABH6v9R500bdZcBV0KUrtWze0uy3GOHDms3t27qHnrdlqyfI2Sp0ihvr263XM57sa54PHt3btHLzd4ScWLFNDwoYM4B0h6IUlsdaqVXW99vkmle/+oo+cua8Tr+SI+z5winl4vlV49pm+J8ufrFEytMtmTPsWKPceihfO1Yf06u8twrKWL5uulauVVsUQBtf7wHZ04fvP648C+vXr3tYaqUqaoPh051OP2U8LQE3TgwH61aN1GlSpXVeIkSVS/YWNt377N7rIc52JgoIYM+lhffzdRk6fPVpduvTR65DBdv35dbVt+qCxZs2vilBk6cGCfFsybY3e5jmFaMipXqRZp2Y0bN7R/317ly59fcePFs6bYsePYVqOTLZw/T3Veqq/CRYspRYqUat2+g37/7X9WaxtuMa09zVq2UcVKVZQ4cRLVa9BIO7Zvu+dy3I1zweMx5wBzwydr9uyaMm2W1Zo2by43d3Kkia//HTynrUfO61jAFU3b8JfSP3/rOD+4SV59s3Kf/joTdNfPJvCLpp4v5dS+kxefctXOd+H8eQ0bPEhp06WzuxRHOnb0sL7+bJT6DxuliTPmK1my5Orfq6u1n3Zq21yZs2TTNxOn6tCB/Vq8YK48CWHoCSpVuowaNGwcMX/o0EGlTvOCrTU5UdDlILXr0Fn+GTNZ85kyZ7EC0oZ1a3Xp0iW1bd9RqVKnUbMWbaxuTbipe88+evmV1yIt27tnt3VHplG9OiqcP7eaNX1HJ04ct61GJzOhJ1ny5BHzPt43D4c+Pr42VuU8JUuVsYJOuL8OHbKOY/dajrtxLng8635aq0sXL6n9R52VOk0atWjVVnNmzZTb7TlxUcUzP6ccqeMrbkxfvVE6vdbu/Nv6rEnxtMqeKr4On7ms8jmTydfbK9LP9qyXS0v+OG6FKUQ2dMgglS1fXrly5bG7FEfas2uXsufMbYWepMmSq2rNOjpy+C9t2vCTLl26qOZtP1LKVGn0frNWWjTPs25a2BKGJkyYoEOHDslNgq9f14Tvxqpho5ftLsVxzN2FKtVqWH+/ERysiePHqky5CtqzZ7dy5MqlmLFiWZ9lzJSZZxJukyp16ruWHTxwQBn8M2rA4GGaNXeRfH191a93T1vqc7rMmbNozaqVEc358+bOUY6cuRQ3bly7S3Os4ODr1v5pWjYeZjnuxrng4e3ZvUu5cudWrH/OAZkyZ+ZZK3PT68RFLfztmH7oWk67R9RU3rSJ1GfWVvnF8FHHmtl08O9LSp4wlt4r56/Z7Usqhu/NS72imZKoeJbn9PEcWiXvtPnnTdq8aaNat+1gdymOlTZ9Bv32y8/as2unFX5mT5+iAoWKaP+e3cqeI7dixry5n2bImFmHDnrWfmpLGHrjjTdUqFAhffDBBzp8+PBj/Y5r164pMDAw0mSWOdWYUSPk5+enl+o3sLsUR5/4KpYtoZ83brBaioIuXVLKlKkiPvfy8pK3j7cCAy/YWqeTVa1eQxO+n6bsOXIqZapU6tiluzZtXG+1sCGy1958S8HBwdbzCK83aaRx336tho2b2F2Wo302eqR1HKvzUoOHWo67cS54eJeiOAf4mHPABXefA15Ml1AVciVX1YGr5N9qnub+ekSTmhdTtbwp5RfDV/U/+UkjFu9S41HrFT9WNNUrnMYKRIOa5FWnyX/o0tUbdv8THMVcO/bt3VNde/RSnDh0K7+XdOkzqHS5Cnr7lfqqUrqIdmzfqg9bt1dQUJCSp0wZ+VrN20cXPehazbZucr/99ptSp06tggULqk6dOlq2bJlCQkIe+ucHDBig+PHjR5qGDBogJ9q4Yb1mTJ9q3a2PFi2a3eU4lmn5+eyrsUqfwV+9e3SRj4+PokWLHuk7MaLH0NUrV22r0dPEjRtPoaGhOnP6ZhcK3BI/fgKNmzRFg4Z9Ym176dKlV5Vq1e0uy7FMqJ41Y6o+Hjg00nHsXstxN84Fj8Y6B0SPfA6IHiOGrlx19zmgZv5UmvfLUf1xKECXr4Vo0LwdSpPET8kSxNJvB8/p/OVg63shoWHaeSxQaZLEVutqWfTnXwFase2k3eU7zldffKYcOXKoZKnSdpfiaNu3/qn1P63Rl+OmaNnazSpfsYo+avVB1NdqMaLrqgftp7aFIdMVpUuXLvrrr79Us2ZN9ejRQ0mTJrVajcaPH689e/bc9+c7d+6sCxcuRJo6dOwspzl65Ig6d2xv3XHI4O9vdzmOZu4mZMmaTT379tea1SsVL358BQScu+v5Ii4i7m3o4AH6YemSiPnt27bK29vb6t+LqD333PNaueJHtWjd1jqo425Hjx5Rt04d1KlbL+tmxYOW426cCx5d/CjOAZeDOAf4envruXgxIubjxPS1WoSi+XgpZvTIx7CUifx09Oxl1SmQWpVypdDO4dWtycwPaJxH/RvzfMziRQu0auVKFS+c35oWL1qo/v166+M+vewuzVFW/LBU5SpWUbYcOa3W7Xc/bGm91iNevPg6f9d+elm+HrSf+tp10RsuRowYevPNN61p3759mjFjhsaOHaumTZvqypUr9/wd5ufMdDuntfyaVNziw/dVtmx5lSlTzjqIG7H8/CKtA7f75edN2rD+J7X6p6+u7z8XpGnTpde8ObceljVDSJv+9iYkIWqZs2TVmNEjrBGrTEvr4AH9VKNWnYg+97jb1MmTrG2tTLnydpfiSOY41rp5U5UuW06ly5TV5cs3j2OmG0RUy2PF4vh2J84Fj8d09719wIRjx45aI1eZkORmv+w/q+Gvvagth/11JvCqGhdLqzOB1zRh7UG9W85fr5ZIp+VbT6pq3hTKnjq+Vn17ypr38bm1rfV4KafVijR9w19yu3ETJutGyK0LyOFDBlvPqtWsXcfWupwmJCRE58/fCj3mOHb1yhX5+Ppox7Zbw7ib4bavB1+3QpKnsCUM3Wv8cX9/f6vFx0ye1Lx2L2asejOkqplmzZwesXzxDysi9YN2O3Mh2r5Nc2t0paLFS+jzMSNVuEgxFS9RSh/37qGFC+aqeo3aGjf2axUsVIS79/dRo2Zta5CJNi2bKXbs2NYFfouWbewuy7HMqIXjx36jT7/4xu5SHGvjhnXWNmWmObNmRCw3z/VFtXzBkuVKwfEtEs4Fjydf/gK6eOmiFsybqxq1amvsN1+pUOGirj8HzPv1qDIkjaN3y/rr+fgxtft4oN7+cpPOXLymJqPXWyPG9aqfU39fuKYPv9lstQzdKejaDZ27dE3ngq7L7ZImSxZp3rR6JEiQUAkTJrKtJifKmTuPBvbpoWlZJihR4sRaOHeWEiZKrHqNmuj78WO1dOE8Va5eS9+P+0b5Cxb2qP3UK8yGNyOZbnBNmjSxRrr6LzmtZcgTBN8IlRNsXL9Ow4cO1N+nTqpw0eLq1KWHEiZKpNUrl6tr5w6K7RdbIaEh+urbCdZoaXbzuWO4UjwEVtljCXXGLupRfG+7Aw7PtnLFcnX+qJ38YsdWaEiIvh0/Sf4OOAeES9/cs4YQdooDY+raXYLHCbxy81kwu4SFhWnc159r0fw5OnvmtNJlyKiO3Xopc9bsWrt6hfp07Si/2H4KDQnV6K/GKZ1Duk4/HzeaM8PQk0IY8twwdD9/nzqlHTu2KXfuvFZAcgLC0GNglT0WwtCjIww9W06Zc8D2rcqd50Ulcsg5IBxh6PEQhjwvDD3I6b9PadeO7cqRO4+jWtUeJgzxdkE43vNJk1oTAMB9zOBKZgLgXM89n9SaPJFto8kBAAAAgJ0IQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABciTAEAAAAwJUIQwAAAABcySssLCxMz4grwXZX4HluhIbaXYJH8vXmPsKjYlt7PN5eXnaX4HGu32BbexwxonFce1ReYv98HPl6/mB3CR7n557l7S7BI8WN8eDjGkc+AAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIAAADgSoQhAAAAAK5EGAIA4Cm6cP68tvzxu84HBNhdCgC4HmHoCZo3d7by5Mh812SWI7LVq1aoVpUKKpQ3h15/uaEOHtivBfPmKH+urHdNZjkiWzh/riqXL60iBfLq/Xfe0LFjR+0uybHY1h7d6pUrVKNyeRXIk12vvdxABw7st5bv27tHrzSqp1JFC+qTYYMVFhZmd6mOMXRgPxXOmy1iqlezkrX8x6WLVa9mZQ0d2Fe1qpaz5hHZ+fMBqlapnI7fdhwz21qThvVU0mxrQ9nWHsbIT4aqZbOmdpfhGLVfTKEd/SveNZnldV5MoXmtimpT9zIa0jCnEvhFi/g5/6RxNO3DQtrYvYzaV84kt1sdxTn0dqNHDFOb5h/IkxCGnqCq1apr7YZfIqZly9coYcKEypevgN2lOcrRI4fVp3tXNW/dVouXr1byFCnUt1d3Va5aTavW/RwxLfphlRIkTKi8+fLbXbKjHDl8WGNGjdAnoz7V7PmLlDx5CvXo2tnushyJbe3RHTlyWL26d1GL1u20dPkaa/vq27Obrl+/rtYtPlDWbNk1adpMHdy/X/O50RNh984dGj7qc/24dpM1jZ8yWxcvBmrYoI/1xdiJmjB1tjp17aVPRw6zu1RHCQgIsC7gjx87FrHMbGutmt/c1r6fOlMH2NYeyITH6VMnq0PHLnaX4hiL/jyhQn1WRkxlBq7RuaDriubjpS41smjgol2qO3qj4sTw1ehX8lg/Yz777NW82nEsUA0+3aQMz8e2gpNbHb3HOfT27W7mtClq19GzrkEIQ09QtGjRFS9evIhpwfy5Klu+olKlTm13aY5i7ip82LK1KlSqosSJk6heg0bauX2btf7ixosXMS1aME9lylVQqlSsv9vt2rVDOXPnti4UzIVqrTp1dfivg3aX5Uhsa4+3zpq3aqOKlasocZIkqtewkXZs36b1P63VpYuX1LZDJ6VOncb6zrw5s+wu1xFu3Lih/fv3Kk++/IobN541xY4dW5eDgtS6Qydl8M9ofc8/U2YFXgy0u1xH6dShrSpXrhZpWfi21u6jTkqd5ua2Nnc229q9mFazvr17qMmrr1vrCzcFh4Tp4tUbEVOtvCm0fPspvfhCQs385Zg27jun4+evaujSPcqXNqHix4qmkpmSKG5MXw1avFtHzl3RiB/2qm7+lHKrg/c4h4Zvd/379lLjV15TqtSetd05Kgxt2bJFv//+u0JCQvSsuXbtmiZPmqC33nnP7lIcp0SpMtYOFe7QoYN3HcDN+pv6/US9+Tbr707pM/jrl583adfOHbp48aKmTZmsQkWK2V2WI7GtPbqSd6yzvw4dUuo0L2jPnl3KmSu3YsWKZS3PmCmzdcceN++OKixMrzWqq1KF86p1s/d08sRxJU2WXJWr1rC+cyM4WJMnfKfSZSvYXa6jdO/ZRy+/+lqkZXt277Ju+IRva5kys63dz6yZ07V79y6lSJlKa1evUnBwsN0lOU50X2+9UiyNvlp9UAljR9OJC1ciPgsJDYv4M3PyuPrzyHldDQ61lu0+eUn+z8eRW5W4zzl0zqwZ2rPr5nb305pV1jHOU9gShv766y+VKVNGqVKl0ltvvaWzZ8+qYMGCKlu2rIoWLSp/f3/98ccfepYsWbRAuXLnVsqUqewuxdGCg69r0vjvVK9h40jLly5eqBy5citFSvfekbmXDBn8Vb5CJTWqX0cliuTX1q1/qm37jnaX5Xhsa4+3ziaMG6v6DRsr6NIl66QXzsvLS94+3gq8cEFud+jgfqXL4K8+A4ZqyqwF8vH11cB+vSI+37t7l6qWL6GfN61Xmw6dbK3VaaLqOXEp6FKkcyfb2r1dvhykT0ePUJo0L+jvUyc1ccJ3evuNV6ybPLileu5k2nL4gtUStOvERZXJ+nzEZ3XypdSWIxd06doNq8vc0YBbQckICQtTvJi+crvg286hZrv7fPRIpXnh5nb3/YTxevfNVz1mu7MlDL377rvKkCGDpk2bpqtXr6pQoUKqWLGizpw5o1OnTildunTWd+7HrODAwMBIk5NX+ozpUyOlaUTts9Ej5efnp7ovNYi0fNaMaXqpfkPb6nKyLX/+oTVrVmnilBnasPk3Va5SXc0/eJcHjB+Abe3RfRq+zuo1kI+Pr6JHv/WQsREjegzrmO52pvXn2wlTlS17Tiswtu/YVZs3bbACZHj3uNFfjrVadfv17Gp3uY7n6+OraGxrD2XFjz/qypUr+nrseL3XtJk+/2qsLgYGWt30cUuDgqk1bfPNATq+++mQ9WzQzGaF9f37BfVuqXT6fuNh67MboWG6fuNmq1C4a8GhihndR2732W3n0JXLb253n3/znd55/0ON+fIbq6eK6XLuCWwJQxs3blSvXr1UrFgxff755zp48KC6dLn5kJ95tsYEoa1bt973dwwYMEDx48ePNA0ZNEBOdPjwX9ZD7oUKF7W7FEfbtHG9Zs+Ypn4Dh8g32q0T35HDf+motf6K2FqfUy1buliVq1RTzpy55OcXW81bttaxo0etbhKIGtvao9u0Yb1mTZ+q/oOGKlq0aNYx1zzsfrugy0HWZ4jMPDMUGhqqM2dOR7RsZM6STd1799fa1SsVGEgLx/3Eix9f58/dva3dvu/iplOnTlrngvjxE1jzvr6+VhfW40cZYTRcmkSx9EJiP23cd9aav3Dlhl758he1nfKn9py8qP1/X7IGW7j5WbASxY4e6edjx/BRcEjkgOT2c+jfp04px13bXaZII0I6mS1hKFGiRFbXOMMEIXMHe+fOnRGfm9ah5MmT3/d3dO7cWRcuXIg0dXDo6BU/LF2ikqVKc5FwH0ePHlH3Th+pU7ee1t3S2/24bKmKlyzFie8ezDN2Z8+ciZgPCgrSlSuXFfoMPnv3X2Bbe3RHjxxR184d1Llbr4h1li1HTm3d8mfEd8xJL/j6devC1e1GDB2k5T8siZjfsX2rvL29dezIYY3+ZEjEctO6Znh7OerxXcfJniOntkSxrZlAjsiSJUt2Vy+ZEyeOW6N+4abKOZNp9a7TVqvP7f6+eE3lsyfViGV7Ff7RtqOByp361naWIkFM63mjC5c953mYp3EOTWptd5Fbak8cP65kyT1ju7PlCNyvXz/VrFlTjRo1srrHvf/++6pdu7Y6dOig1157Td26dbOeJbqfGDFiRBqpzUxmmRNtWP+T8hcsZHcZjmW6Opgx6UuXLadSZcpafU/NFN7NayPr777y5HlRK1f8qIkTxmnxogVq0/JDa5QXczcQkbGtPd46a9W8qUqXKafSZW+ts7wv5tOlSxetd1wZ3337tQoWLiIfH7qPZMqcRV+MGanf//erft28ScMG91e1GrWVMXNWzZk1XXNnTdepkyf02ajh1mAnceLGtbtkR3sxX35rW1sw7+a2Nvabr63WW7a1u5UoVdp6D9iMaVN06uRJa+Cm3bt2qmjxEnaX5hjFMyXR5gPn7lrepEgaHTwdpBU7b7bgGr8eClCcmNFUM+/NG/SmC50Zde6OHCW3n0OLlSipgwcPaOb0qdZ2ZwYhMgOfFC3mGdudV5hNDxYcOHDAGjkuW7Zsypo1qzZt2qTp06dbQ5Ka7nMNGz56n/0rwc7ccMxD7dNnzVO69BnkNDdC7W/qXb1yudq3bnHX8vlLlitR4sQqU6ygpsycq7Tp0sspfL2dcyfX7MJffv6pNazx6dOn5Z8xo3r06qts2XPISdjWHo+3l5et//1VK5arXevmdy1fuHS5du/apa6d2lvdM0NDQ/T1dxMjho220519/O1ggs6cmdPlF9tPpcqU1wctWitWLD9t2rBOI4YO1N9/n1LhIsXUoXMPJUyUSE4QI5pzjmt5c2TRomXLIwbpMNthl463trVvxjljW/OSvfvnvZ4jHTZkoBWCzHD47Tp0Vtly5eUk+Xr+YMt/N4avt37uUVZ1Rm/QwdOXI5ab4bOXtS+u9777TduORR7uvlzW5zS4YS4FXb8hHy8vvf71L9r3d9BTr/3nnuUdfQ49e+a09fJts90lSZxEbTp0VOmy9tccN4a3c8PQk+DEMOR0TrhA9UROCkOegm3NM8PQg5i+4ua9Q7nz5HXMRb0TwpAnclIYute2tv2fbc10t3cCJ4YhT2BXGHpcz8eLoRwp4+n3w+cVEGTPxaYTwpAnepgwxNiAAIDH9nzSpNYEPGlsa7DL34HXtDLwVvc5PFucfRsIAAAAAJ4QwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAVyIMAQAAAHAlwhAAAAAAV/IKCwsL0zPicvAz8095arzkZXcJHsmL1fbIQkPZPx/HleAQu0vwOH7Rfe0uwSNxXHt012+E2l2CR/L1YWN7VP4t5thdgkc6/kXdB36HliEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgAAAOBKhCEAAAAArkQYAgBJ588H6I8/flNAQIDdpXiUC+fPa+ufv+s86w2w/Rj25x/si8CjIgw9QfPmzFK92jVUokgBderQloushzBv7mzlyZH5rsksx4NPhFUqltWxY0ftLsXx66la5XI6ftt6WrpkkWpWraSBH/dV1YplrHnc7bNRw9Wh1YcR8z8uW6wGtapo6MB+qlOtvDWP+xv5yVC1bNbU7jLwjPlhySLVrV5Zg/v3VfXKZa35BfPmqEDurHdNZjluOx9Uinw+WLVyhapXLq/8ubPr1cYNdGD/frnZS4VS65f+lbV3RE1Na1VcqRL7Rfq8S+3sGv9hkbt+LmHs6NrUr9Jd33ciwtATsmnjBg0e8LHaf9RJ02bNVdClILVr1dzushyvarXqWrvhl4hp2fI1SpgwofLlK2B3aY4WEHBOLT5squPHjtldiqOZGxLmQvT29XQxMFCDBvTT2PGTNHXGHHXt0VsjPxlma51OtH/fXs2eMVWt2ney5i9eDNQng/rrs28naPyUWfqoS099NnK43WU62r69ezR96mR16NjF7lI8xt69e/Ryg5dUvEgBDR86SGFhYXaX5DjmGDZk0Mf66ruJ+n76bHXp1kujRw5T5arVtPKnnyOmhT+sUoKECZU3X367S3bs+eDI4cPq1a2LWrZup2Ur1ih5ihTq07Ob3OqFJLHVqVZ2vfX5JpXu/aOOnrusEa/ni/g8c4p4er1UevWYviXSzyWKHV3jmxVRmiSx5QkIQ0/IwvnzVOel+ipctJhSpEip1u076Pff/mfdhcC9RYsWXfHixYuYFsyfq7LlKypV6tR2l+ZoHdu3VeUqVe0uw/FMC23lKtUiLQu6HKQOH3WRf8ZM1nymTFl0MfCCTRU6k7kAHfxxLzV8+VWlSp3GWnY5KMgKRhn8M1rzGTNltgIS7r0O+/buoSavvq7UaW6uQ9zf9evXrYvVrNmza8q0WdYdenoJ3M0cw9p26BxxDMuY2RzDAq3zadx48SKmxQvmqUzZCkqVivNpxPmgcuTzwcED+9W8VRtVrFxFiZMkUf2GjbRj+za5VY408fW/g+e09ch5HQu4omkb/lL65+NEfD64SV59s3Kf/joTFOnnPn+noOb96jm9VAhDT4gJPcmSJ4+Y9/G+uap9fHxtrMqzXLt2TZMnTdBb77xndymO16NXX+siC/fXvWcfvfzKa5GWJUuWXFWr17D+HhwcrAnjx6psuQo2VehM82bP0N7du5U8RSqtX7taN4KDlTRZclWqWt363MxPnjhOpcuWt7tUx5o1c7p2796lFClTae3qVda2hvtb99NaXbp4Se0/6mwFyBat2mrOrJl2l+U45hhWpVqNiH1x0vixKn3HMcycT6d+P1FvvM35NNL54NXI54OSpctYASjcoYOHlDrNC3KrPScuqnjm55QjdXzFjemrN0qn19qdf1ufNSmeVtlTxdfhM5dVPmcy+Xp7Rfxch+9/07crPad7obedzZMXL17Usypz5ixas2plRJP+vLlzlCNnLsWNG9fu0jzGkkULlCt3bqVMmcruUhyPlrN/v57MhWr50sW1acN6ujHd5vLlIH312WjrYvT03yc1ZdJ4ffDO69bFlbF3zy5Vq1BSmzetj+hCh7vX4aejRyhNmhf096mTmjjhO739xisR6xBR27N7l3UOiBUrljWfKXNm1z+/8aD1ValsCaubfrsOnSN9tmzxQuXIlVspUqa0rT5PO28GB1/XxHFj1aBRY7nV3hMXtfC3Y/qhazntHlFTedMmUp9ZW+UXw0cda2bTwb8vKXnCWHqvnL9mty+pGL43Y4UJSJ7kqYehs2fPqmTJkkqSJIkSJEigIkWKaPLkyY/cD9icRAIDAyNNTjqxvPbmW9adP9PX+fUmjTTu26/VsHETu8vyKDOmT1W9Brfu0ABPUqZMmfXlN98pg7+/enYnDIVbs3K5rl65otFfjtWb736gEZ99rUsXL2rJwnnW5/4ZM2vUF2OVLr2/Pu7l3r7197Pixx915coVfT12vN5r2kyffzXW6sZkugHj3i5duhTpZpiXl5d8fLwVeIFurFExXVU/+2qs0mfwV58ekY9hs2ZMU936DW2rzRN9OmqkYvn5qW69BnKrF9MlVIVcyVV14Cr5t5qnub8e0aTmxVQtb0r5xfBV/U9+0ojFu9R41HrFjxVN9Qp7Zhfgpx6GWrZsaT0Lsm3bNu3YsUOlSpXSK6+8ohw5cmjBggUP/XsGDBig+PHjR5qGDhogp4gfP4HGTZqiQcM+sQ5Q6dKlV5VqN7uU4MEOH/7LepCxUOGidpcClzAXWlmyZlPvfgO0etUKLrj+8fepU8qWI6fixU9gzfv6+lrPJpw4fixivWXOklXden+sn1avVCDPW93l1KmTypkzl3VeCF+H5rxw/Kjn9Km3g4+Pj6JFjx5pWfQYMXTl6lXbanIya1/Mmk09+/bXmtv2xSOH/9LRI4dVqNDdI34haqaHwMzpU9V/8FBFixZNblUzfyrN++Wo/jgUoMvXQjRo3g6lSeKnZAli6beD53T+8s3uviGhYdp5LNBjBkywPQwtWbJEn3/+ubJmzarMmTNr4MCBVuuQaS165513VLp0af32228P/D2dO3fWhQsXIk3tO0ZuFnaC5557XitX/KgWrdtaB3Y8nB+WLlHJUqVdfRDC07H55036ZNjgiPnw/dTrn+f83O75pMnuanU/eeK4tXzMJ0MjloU/D+ntxXq7U7Jkd6/DEyeOWyNV4d7MTU4zUubtzMAdnBci++XnTRo5fEjEvO8/x7DwfXH5D0tVvGQp+bLeHsrRI0fUpVMHdeneSxky+MvNfL299Vy8GBHzcWL6Wi1C0Xy8FDN65GvalIn8dPSsZ3WPC/fUz1opU6bU3r17Iy0zJ4kOHTro0KFDqlKliipVqvTA3xMjRoxIo46ZySxzmqmTJyltuvQqU44Hix/FhvU/KX/BQnaXARdImy6d1YXETCdPntCoEcNUpGgxnu/7R7ESJfXXwQOaM3Oa9bzL9CmTrOeEMmXOormzp2vurOk6dfKEPh89XAULF1Uc1ttdSpQqrQMH9mvGtCk6dfKkNTDM7l07VbR4CbtLc7TsOXJq659/Rsybd6iZEeZMSMIt5hrD7J+zZ063jmFjRg5XoSLFIvbFjet/Ur4CnE8fxtWrV60RDMuULafSZctaz/uZya1Duv+y/6yq5k2hd8v5q06BVBrbtLDOBF7ThLUHlTFZXL1aIp2SJ4ilt8tkUPbU8bVqxyl5oqcehtq2bavatWurXbt2GjlypMqVK2c9HJk+fXrrz44dO2rfvn16Fpg+4ePHfqN27TvaXYrHHYy2bvlTuXPnsbsUuMDzzyfV4GEj9P2kCapXu7quXrmqvv1vtRS5nekeN3zMl1q6aL4a1qmm6ZMnqveAocqZO6/6DRpuzTepX8vab3v0G2h3uY5kusd99sU3WrhgnmpVr6TvJ43XwCGfMDjMA+TLX0AXL13Ugnk3n60a+81XVtdpellE9tzzz2vAkBGa+v0ENapbw9oX+3w8yPrM/H3b1i3Kxfn0oWxcv84aXnv2zBkqVjBfxBTeLdht5v16VJ8u26N3y/rrk9fzK75fdL395SaduXhNTUavt54RWtengt4p668Pv9nssS1DXmE2xN3ly5dr4sSJOnPmjPLkyaP27dtbL9b8ty4HuzO5/xteujUUIh6eF6vtkYWGsn8+jivBIXaX4HH8ovMKg2fluLZyxXJ1/qid/GLHVmhIiL4dP0n+/7zbygmu3wi1uwSP5OvjwI3N4fxbzLG7BI90/Iu6zgxDTwph6NERhp6diwanIww9HsLQoyMMPVvHtVOnTmnH9q3KnedFJUqUSE5CGHo8hKFHRxh6cmGIMwYAAHCspEmTWhMAPAkM+wMAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMAQAAADAlQhDAAAAAFyJMATg/+3dCbDN9RvH8ce+3sUu5dpHlpElSzSmGiTLSEmWyZYohbKUGw1FIhKlbEkxRZu9SJYae5GUiOykLHW76Loo5z+f78w5c4W7/bv3d3/O+zVz3Ht+997jO985y+/5Ps/3+QEAAIQlgiEAAAAAYYlgCAAAAEBYIhgCAAAAEJYIhgAAAACEJYIhAAAAAGGJYAgAAABAWCIYAgAAABCWCIYAAAAAhCWCIQAAAADhKYAMl5iYGBg+fLj7itRhztKHeUs75ix9mLe0Y87Sh3lLO+YsfZi38JyzbPrH64Dsenf69GmLioqy+Ph4i4yM9Ho4vsCcpQ/zlnbMWfowb2nHnKUP85Z2zFn6MG/hOWeUyQEAAAAISwRDAAAAAMISwRAAAACAsEQwlAny5Mljw4cPd1+ROsxZ+jBvacecpQ/zlnbMWfowb2nHnKUP8xaec0YDBQAAAABhicwQAAAAgLBEMAQAAAAgLBEMAQhrv//+u23YsMFOnTrl9VAAAEAmIxjKYDt27LC6detaoUKFbPDgwcYWrdSfoJYrV84OHjzo9VB8Y9GiRVa+fHnLmTOn1a9f33bt2uX1kLK8efPmWcWKFe3xxx+3mJgYdx+p17x5c3vnnXe8HoYv9O3b17Jlyxa66XmH1BsyZIi1bt3a62FkeXo9Jn2eBW+8TpM3Z84c9xlQsGBBa9KkCeceqTRr1iyrXr26RUdHW8eOHX27qEgwlIHOnz/v3rzr1KljW7ZssZ07d/KGlAp6MbVq1Yo3ozTYt2+fde/e3caMGWO//PKLlSlTxnr27On1sLK0P//8052grl271rZt22bTpk2zZ555xuth+cZ7771nn3/+udfD8I2tW7fap59+anFxce6m5xxSv6j45ptv2sSJE70eSpbXqVOn0HNMtyNHjljRokWtcePGXg8tS39+Dh061BYuXOjO0/T52a1bN6+HleWtXLnS+vXrZ6+++qpt377dTp8+bW3btjU/IhjKQMuWLbP4+HibMGGCVahQwUaPHm0zZ870elhZXocOHdwNqacskJ5f7du3txIlSthjjz3mAnBc25kzZ9zJlVa15JZbbnEnD0jZH3/8YQMHDrTKlSt7PRRf+Pvvv90JvU5ItYKqW0REhNfD8gVVU/Tu3duefPJJ9zmK5OXOnTv0HNNt9uzZdt9997mqAVydFiYaNGhgtWvXdtkhLSzu2bPH62FlebNnz3aLrk2bNnUB5Lhx42zdunWussdvCIYykCJlvcDy58/v7teoUcOtOiB506dPt/79+3s9DF9RJu3RRx8N3d+9ezdlOCkoXbq0de7c2X1/8eJFGz9+vDtpQMoUCGkFUO9vSNn333/vTupr1qxp+fLlc+WFhw8f9npYvjBjxgz77rvvXNn00qVL3WsVqZOYmGiTJk2y2NhYr4eSpVWtWtVWr17tgiItYL/xxhvuBB8pV/EoeAzKkSOH+6pSfb8hGMpAShnqDTxIdbt6srD6nDxWsP4/Fy5ccCf2ffr08Xoovlm0UDZtxYoVlOGkwpo1a2zVqlU2duxYr4fiq8xttWrVbO7cuW5BLFeuXC7bgeSdPXvWhg0bZpUqVbKjR4+6Kgtl13SSj5S9//77bsGibNmyXg8lywdD7dq1c5khZdM2b97sPkORPC3uLF68OLQXXvuH6tWrZ1FRUeY3BEMZSNHxv6/ImzdvXktISPBsTLj+6eRBm0B79erl9VB8QRlbndzrZFXlEbg2nYTqJH7KlCkWGRnp9XB8QxnITZs2uWY6WiCbPHmyC761YIZrmz9/vv31119u1f65555zc6a9firPQcqmTp16WcUArk6vzSVLlrggSOXTagTQokULGl6lYNCgQW7xVfviGzZs6BbInnjiCfMjgqEMVLhwYTt58uRlx/RCU00vkBG++OIL9wGoFUGtPiNlytjWqlXLNTdRRz4yt9c2cuRId0LfsmVLr4fia1p9vnTpkv36669eDyVLUzZInTH1WRpcYNTixYEDB7weWpa3d+9ed1NnNCTvgw8+cPuUldXQQuKoUaNs//79rmoA16bX5fr16+3DDz90r8ubb77ZNfDwI4KhDKSTBq04BKk7mjrMBd/Ygf+S3ry1Aq1Ve6X9kTytNqvdfVCwzjl7dt4Wr0VBtgLG4OZs3Vc5JiWZyRswYIA7YQj65ptv3PNM+9ZwbZqfc+fOXXbs0KFDbrM2kqfnm/aSsiiWugYnx48fv2zRWhnJf/75x9Nx+UWpUqVcFvell14K7RvyG//tcvIR1TZrM55S+l26dHFtj7VK49cnC7IunTDog+/ee++1Nm3auFp7KVCggMt84EpaxdJ8aT/CPffc48oLmzVr5st658yiNuQ6cUhaJqE9CbShTbm2Xq17S5Ys6eZPLd01Z8HmOrg6ZSA1V8p26/1NJ1xqpqAGFEje8uXLKftNpUaNGlmPHj1ci2jtH33rrbfcV2U7kLLXX3899HnqWwFkqAULFgTy5csXKF68eKBIkSKBHTt2eD0k39DT88CBA14PwzfPM83Xv2/MX/KWL18eqFKlSiAiIiLQrl27wIkTJ7wekq907do1MGvWLK+H4QtDhgwJREdHB0qXLh3o169f4OzZs14PyRc2btwYaNiwofscLVeunHuvQ/ISEhICuXPnDuzatcvrofjCpUuXAiNGjAjExMQEcuXKFahVq1Zgy5YtXg/LF+Li4gKFCxcOfP311wE/y6Z/vA7Irne6CKau+aINZsWKFfN6OAAAAAC0d5hgCAAAAEA4YqcwAAAAgLBEMAQAAAAgLBEMAQAAAAhLBEMAAAAAwhLBEAAAAICwRDAEAPDM+fPnr7jSu5qc6nha6G8uXbr0n4xJF0b96quvrni83bt3uyvTAwCuH7TWBgBkihtvvNEiIiIsb968Fh8fbw888IC7Dtu2bdvswoUL7vvKlSu7IET3d+7c6a5q/sgjj1jr1q1dgJL0I0tXia9SpYr7fuLEibZ582abO3du6OfDhg1zj/Pyyy+7gOvixYvu/05Kx7Jnz245cuQIHZs1a5b179/fli1bZoUKFbLcuXNb2bJl3VXWdbxv375uHAqacuXKlSlzBwDIGDkz6HEBALiMgh05ePCg1atXz7p27WrVqlVzxz777DMbN26crVmz5rK/6d69u3Xp0sXmz59vrVq1so4dO7rjP//8s5UvX95eeOEFi4uLszx58rhbUvnz5w8FOXrcli1bWsGCBS1btmyhQEiZnpUrV9odd9zhju3fv9+efvppi4mJsbvvvtuN77bbbrMyZcrY8ePHbdSoURYbG2vR0dHu5zNnzsyEmQMAZBSCIQBApkhMTLSRI0favn377Pnnnw8FQnLs2DGrWLHiFX/Ttm1bq1+/vt1www2WL18+e+211+zw4cO2adMmW7duna1YscJ91e8EnThxwmWedFO53Z49e6xu3boplt7pMdu3b299+vSxAQMGWKlSpVx2SGNr0qSJbdy40QVcTZs2tb1791rOnHyEAoDf8U4OAMg0P/zwg23YsMFmz54dCkB69uxpp06dcqVsCjikX79+1q5dO1u6dKnLDCXNLt166602efJkd1+Zn6QlbqJMz5dffmkLFy50GZ0zZ864UrsGDRpcdUz6f/UYFSpUsBdffNEeeughd1xBmzJSyhS9++67Vr16dXf87bffdqV1AAD/490cAJDhFHCoPO2TTz6xHj16uL08on03RYsWtd9++81Wr17t9v2oHE4BzMmTJ23EiBEuUxNssqA9PypRS06nTp1s6tSpLhOk0rgJEyZY7969LSoqyv1f+hoZGem+11eVxEmxYsVCgVBCQoK98sorLoM1Y8YMl9FSiZzK6u666y6CIQC4TvBuDgDIcGqSoMxK1apVXambsjQKjoL7iKRZs2YuGyPK1KiZwvr1693en2DwocYFKpdLyapVq+z06dM2Z84ctzfp22+/dWVzykANHjzYBVj6Xr8THIMee/v27TZ8+HBXsqc9Qhr3/fffb2vXrnUlciq3GzRokNuDpGANAOBvBEMAgAyn0jY1PViyZIkLNFR+Vq5cOVfGlnRPkUrVktJeIe3fUeCkLnMKYIoUKeJ+llwrbQVceiwFMiq9Uye6xYsXXxZ8aS9RsCxPlEFSs4SjR4+6YGnevHlWqVIll0FSJzxlqTTm2rVr26RJk6xbt26XdbcDAPgPe4YAAJmuZMmSLjhROZwoa6PMS4ECBa4IdNTUoFevXnbu3DnXuECNDUSldle7vpD2GSkTpH1CoqCmZs2aLjgK7lUSBWTTp0+3HTt2uOyTWmZr/5LK6PTYas196NAh12HuzjvvdH8zbdo0u/32210pHgDA/8gMAQAynfbqqGwu2OFty5YtLuuSNEskKlv78ccfrU2bNvbTTz/Z8uXLrU6dOq6kToGM9hwF9x8F6bj2+QSvKaRARvuRFIBpD1HQwIED3e9oP5DoekIKhNSNTmNT621lktRAQfS9GjvoOADg+kAwBADwTI0aNVyjAgUwzZs3d9kaXdhUDQ9k9OjRLgBRkKLA5aOPPnIXbm3UqJGdPXvWXbhVF1VNSo/RokWL0H0FTNoH9NRTT4WuMSS6YOrQoUNtypQpLjMVVLx4cRcMLViwwLXs3r17t8tgffzxxy4o+3cpHwDAvwiGAACZQgGHrhGU9Po8CnIUvKhRgYIetcN+8MEHXbZHWSDtMdJxUeMCdZrT/caNG7sLsqqznMrmgpkkCT6+MjjqQqefde7c2Tp06GCLFi1yzRCCTRj0f6lMTuOQYMmdgiQ1e1AjB7XqVgmfLrD68MMPhwIsAID/sWcIAJApxo8f767XEyw727p1qwsuSpQo4brGFSpUyO3pUVtsNTLQ7z777LOuYYKOKyjR38jEiRNdlzgFWGp8MGbMGBs7duxl/59K8BQQFSxY0GJjY90x/Y4CKAVHwcDppptuCv2NSuX0+1e7oKqCqo4dO7rH1U3NHFJq8w0AyNqyBWiFAwDwgLIwuuhqw4YNr/jZkSNHrHTp0qH7+qg6duyY6+r2b7pGkYKTpJ3pAABIDYIhAAAAAGGJPUMAAAAAwhLBEAAAAICwRDAEAAAAICwRDAEAAAAISwRDAAAAAMISwRAAAACAsEQwBAAAACAsEQwBAAAAsHD0PyWzfAOI+QVzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LDA 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.96      0.95       980\n",
      "           1       0.89      0.97      0.93      1135\n",
      "           2       0.92      0.79      0.85      1032\n",
      "           3       0.87      0.87      0.87      1010\n",
      "           4       0.84      0.90      0.87       982\n",
      "           5       0.84      0.82      0.83       892\n",
      "           6       0.91      0.89      0.90       958\n",
      "           7       0.91      0.84      0.88      1028\n",
      "           8       0.80      0.81      0.80       974\n",
      "           9       0.81      0.85      0.83      1009\n",
      "\n",
      "    accuracy                           0.87     10000\n",
      "   macro avg       0.87      0.87      0.87     10000\n",
      "weighted avg       0.87      0.87      0.87     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MLP 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.97      0.97      0.97      1032\n",
      "           3       0.96      0.98      0.97      1010\n",
      "           4       0.97      0.98      0.97       982\n",
      "           5       0.97      0.98      0.98       892\n",
      "           6       0.98      0.98      0.98       958\n",
      "           7       0.97      0.97      0.97      1028\n",
      "           8       0.98      0.96      0.97       974\n",
      "           9       0.97      0.96      0.97      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# 设置中文字体（如SimHei、Microsoft YaHei等）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 或 ['Microsoft YaHei']\n",
    "# 解决负号显示为方块的问题\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 创建一个函数来显示混淆矩阵\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, title):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    plt.title(f'混淆矩阵 - {title}')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.show()\n",
    "\n",
    "    # 打印分类报告\n",
    "    print(f\"\\n{title} 分类报告:\")\n",
    "    print(classification_report(y_true, y_pred))\n",
    "\n",
    "\n",
    "# 获取所有模型的预测结果\n",
    "y_pred_logistic = model_logistic.predict(X_test_flat)\n",
    "y_pred_lda = model_lda.predict(X_test_flat)\n",
    "y_pred_mlp = model_mlp.predict(X_test_flat)\n",
    "\n",
    "# 可视化混淆矩阵并打印分类报告\n",
    "plot_confusion_matrix(y_test, y_pred_logistic, \"Logistic 回归\")\n",
    "plot_confusion_matrix(y_test, y_pred_lda, \"LDA\")\n",
    "plot_confusion_matrix(y_test, y_pred_mlp, \"MLP\")\n",
    "\n",
    "# 比较模型性能\n",
    "models = ['Logistic 回归', 'LDA', 'MLP']\n",
    "accuracies = [accuracy, accuracy_lda, accuracy_mlp]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(models, accuracies)\n",
    "plt.ylim(0.85, 1.0)  # 调整y轴范围以便更好地显示差异\n",
    "plt.title('模型准确率比较')\n",
    "plt.ylabel('准确率')\n",
    "for i, v in enumerate(accuracies):\n",
    "    plt.text(i, v + 0.005, f'{v:.4f}', ha='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DataAnalysis",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
